DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.5.0.9039
-Date: 2021-03-08
+Version: 1.5.0.9031
+Date: 2021-03-05
 Title: Antimicrobial Resistance Data Analysis
 Authors@R: c(
     person(role = c("aut", "cre"), 

NAMESPACE

@@ -1,10 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method("!",mic)
 S3method("!=",mic)
 S3method("%%",mic)
 S3method("%/%",mic)
-S3method("&",mic)
 S3method("*",mic)
 S3method("+",mic)
 S3method("-",mic)
@@ -37,10 +35,6 @@ S3method("[[<-",mic)
 S3method("[[<-",mo)
 S3method("[[<-",rsi)
 S3method("^",mic)
-S3method("|",mic)
-S3method(abs,mic)
-S3method(acos,mic)
-S3method(acosh,mic)
 S3method(all,mic)
 S3method(all.equal,isolate_identifier)
 S3method(any,mic)
@@ -48,16 +42,11 @@ S3method(as.data.frame,ab)
 S3method(as.data.frame,mo)
 S3method(as.double,mic)
 S3method(as.integer,mic)
-S3method(as.matrix,mic)
 S3method(as.numeric,mic)
 S3method(as.rsi,data.frame)
 S3method(as.rsi,default)
 S3method(as.rsi,disk)
 S3method(as.rsi,mic)
-S3method(asin,mic)
-S3method(asinh,mic)
-S3method(atan,mic)
-S3method(atanh,mic)
 S3method(barplot,disk)
 S3method(barplot,mic)
 S3method(barplot,rsi)
@@ -68,30 +57,14 @@ S3method(c,mic)
 S3method(c,mo)
 S3method(c,rsi)
 S3method(ceiling,mic)
-S3method(cos,mic)
-S3method(cosh,mic)
-S3method(cospi,mic)
-S3method(cummax,mic)
-S3method(cummin,mic)
-S3method(cumprod,mic)
-S3method(cumsum,mic)
-S3method(digamma,mic)
 S3method(droplevels,mic)
 S3method(droplevels,rsi)
-S3method(exp,mic)
-S3method(expm1,mic)
 S3method(floor,mic)
 S3method(format,bug_drug_combinations)
-S3method(gamma,mic)
 S3method(hist,mic)
 S3method(kurtosis,data.frame)
 S3method(kurtosis,default)
 S3method(kurtosis,matrix)
-S3method(lgamma,mic)
-S3method(log,mic)
-S3method(log10,mic)
-S3method(log1p,mic)
-S3method(log2,mic)
 S3method(max,mic)
 S3method(mean,mic)
 S3method(median,mic)
@@ -112,31 +85,18 @@ S3method(print,mo_renamed)
 S3method(print,mo_uncertainties)
 S3method(print,pca)
 S3method(print,rsi)
-S3method(prod,mic)
 S3method(quantile,mic)
 S3method(range,mic)
 S3method(rep,mo)
-S3method(round,mic)
-S3method(sign,mic)
-S3method(signif,mic)
-S3method(sin,mic)
-S3method(sinh,mic)
-S3method(sinpi,mic)
 S3method(skewness,data.frame)
 S3method(skewness,default)
 S3method(skewness,matrix)
 S3method(sort,mic)
-S3method(sqrt,mic)
 S3method(sum,mic)
 S3method(summary,mic)
 S3method(summary,mo)
 S3method(summary,pca)
 S3method(summary,rsi)
-S3method(tan,mic)
-S3method(tanh,mic)
-S3method(tanpi,mic)
-S3method(trigamma,mic)
-S3method(trunc,mic)
 S3method(unique,ab)
 S3method(unique,disk)
 S3method(unique,isolate_identifier)
@@ -309,7 +269,6 @@ export(theme_rsi)
 importFrom(graphics,arrows)
 importFrom(graphics,axis)
 importFrom(graphics,barplot)
-importFrom(graphics,hist)
 importFrom(graphics,legend)
 importFrom(graphics,mtext)
 importFrom(graphics,plot)
@@ -318,10 +277,8 @@ importFrom(graphics,text)
 importFrom(stats,complete.cases)
 importFrom(stats,glm)
 importFrom(stats,lm)
-importFrom(stats,median)
 importFrom(stats,pchisq)
 importFrom(stats,prcomp)
 importFrom(stats,predict)
 importFrom(stats,qchisq)
-importFrom(stats,quantile)
 importFrom(stats,var)

NEWS.md

@@ -1,5 +1,5 @@
-# AMR 1.5.0.9039
-## <small>Last updated:  8 March 2021</small>
+# AMR 1.5.0.9031
+## <small>Last updated:  5 March 2021</small>
 
 ### New
 * Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.
@@ -73,7 +73,6 @@
 * Support for GISA (glycopeptide-intermediate *S. aureus*), so e.g. `mo_genus("GISA")` will return `"Staphylococcus"` 
 * Added translations of German and Spanish for more than 200 antimicrobial drugs
 * Speed improvement for `as.ab()` when the input is an official name or ATC code
-* Added argument `include_untested_rsi` to the `first_isolate()` functions (defaults to `TRUE` to keep existing behaviour), to be able to exclude rows where all R/SI values (class `<rsi>`, see `as.rsi()`) are empty
 
 ### Other
 * Big documentation updates

R/aa_helper_functions.R

@@ -961,8 +961,15 @@ formatted_filesize <- function(...) {
 }
 
 create_pillar_column <- function(x, ...) {
-  new_pillar_shaft_simple <- import_fn("new_pillar_shaft_simple", "pillar")
-  new_pillar_shaft_simple(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",
+              ...)
+  }
 }
 
 # copied from vctrs::s3_register by their permission:

R/ab.R

@@ -105,6 +105,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
   already_regex <- isTRUE(list(...)$already_regex)
   fast_mode <- isTRUE(list(...)$fast_mode)
   
+
   x_bak <- x
   x <- toupper(x)
   x_nonNA <- x[!is.na(x)]

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 Stable Lifecycle
+#' @inheritSection lifecycle Maturing 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/first_isolate.R

@@ -37,14 +37,13 @@
 #' @param col_keyantibiotics column name of the key antibiotics to determine first (weighted) isolates, see [key_antibiotics()]. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use `col_keyantibiotics = FALSE` to prevent this.
 #' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again. The default of 365 days is based on the guideline by CLSI, see *Source*. 
 #' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
-#' @param icu_exclude logical to indicate whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`)
+#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`)
 #' @param specimen_group value in the column set with `col_specimen` to filter on
 #' @param type type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see *Details*
-#' @param ignore_I logical to indicate whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see *Details*
+#' @param ignore_I logical to determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see *Details*
 #' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see *Details*
 #' @param info print progress
-#' @param include_unknown logical to indicate whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
-#' @param include_untested_rsi logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use `include_untested_rsi = FALSE` to always return `FALSE` for such rows. This checks the data set for columns of class `<rsi>` and consequently requires transforming columns with antibiotic results using [as.rsi()] first.
+#' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
 #' @param ... arguments passed on to [first_isolate()] when using [filter_first_isolate()], or arguments passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
 #' @details 
 #' These functions are context-aware. This means that then the `x` argument can be left blank, see *Examples*.
@@ -160,7 +159,6 @@ first_isolate <- function(x = NULL,
                           points_threshold = 2,
                           info = interactive(),
                           include_unknown = FALSE,
-                          include_untested_rsi = TRUE,
                           ...) {
   if (is_null_or_grouped_tbl(x)) {
     # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
@@ -190,7 +188,6 @@ first_isolate <- function(x = NULL,
   meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
   meet_criteria(info, allow_class = "logical", has_length = 1)
   meet_criteria(include_unknown, allow_class = "logical", has_length = 1)
-  meet_criteria(include_untested_rsi, allow_class = "logical", has_length = 1)
   
   # remove data.table, grouping from tibbles, etc.
   x <- as.data.frame(x, stringsAsFactors = FALSE)
@@ -475,14 +472,6 @@ first_isolate <- function(x = NULL,
   }
   x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE
   
-  # handle isolates without antibiogram
-  if (include_untested_rsi == FALSE && any(is.rsi(x))) {
-    rsi_all_NA <- which(unname(vapply(FUN.VALUE = logical(1), 
-                                      as.data.frame(t(x[, is.rsi(x), drop = FALSE])),
-                                      function(rsi_values) all(is.na(rsi_values)))))
-    x[rsi_all_NA, "newvar_first_isolate"] <- FALSE
-  }
-  
   # arrange back according to original sorting again
   x <- x[order(x$newvar_row_index), ]
   rownames(x) <- NULL

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 Stable Lifecycle
+#' @inheritSection lifecycle Maturing 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 Stable Lifecycle
+#' @inheritSection lifecycle Maturing 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

@@ -25,51 +25,13 @@
 
 #' Transform Input to Minimum Inhibitory Concentrations (MIC)
 #'
-#' 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.
+#' 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.
 #' @inheritSection lifecycle Stable Lifecycle
 #' @rdname as.mic
-#' @param x character or numeric vector
+#' @param x 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.
-#' 
-#' 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()], [log1p()], [log2()], [log10()], [max()], [mean()], [min()], [prod()], [range()], [round()], [sign()], [signif()], [sin()], [sinh()], [sinpi()], [sqrt()], [sum()], [tan()], [tanh()], [tanpi()], [trigamma()] and [trunc()]. Some functions of the `stats` package are also implemented: [median()], [quantile()], [mad()], [IQR()], [fivenum()]. Also, [boxplot.stats()] is supported. Since [sd()] and [var()] are non-generic functions, these could not be extended. Use [mad()] as an alternative, or use e.g. `sd(as.numeric(x))` where `x` is your vector of MIC values.
-#' @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.
+#' @return Ordered [factor] with additional class [`mic`]
 #' @aliases mic
 #' @export
 #' @seealso [as.rsi()]
@@ -118,29 +80,29 @@ as.mic <- function(x, na.rm = FALSE) {
     x <- gsub("\u2264", "<=", x, fixed = TRUE)
     x <- gsub("\u2265", ">=", x, fixed = TRUE)
     # remove space between operator and number ("<= 0.002" -> "<=0.002")
-    x <- gsub("(<|=|>) +", "\\1", x, perl = TRUE)
+    x <- gsub("(<|=|>) +", "\\1", x)
     # transform => to >= and =< to <=
     x <- gsub("=<", "<=", x, fixed = TRUE)
     x <- gsub("=>", ">=", x, fixed = TRUE)
     # dots without a leading zero must start with 0
-    x <- gsub("([^0-9]|^)[.]", "\\10.", x, perl = TRUE)
+    x <- gsub("([^0-9]|^)[.]", "\\10.", x)
     # values like "<=0.2560.512" should be 0.512
-    x <- gsub(".*[.].*[.]", "0.", x, perl = TRUE)
+    x <- gsub(".*[.].*[.]", "0.", x)
     # remove ending .0
-    x <- gsub("[.]+0$", "", x, perl = TRUE)
+    x <- gsub("[.]+0$", "", x)
     # remove all after last digit
-    x <- gsub("[^0-9]+$", "", x, perl = TRUE)
+    x <- gsub("[^0-9]+$", "", x)
     # keep only one zero before dot
-    x <- gsub("0+[.]", "0.", x, perl = TRUE)
+    x <- gsub("0+[.]", "0.", x)
     # starting 00 is probably 0.0 if there's no dot yet
     x[!x %like% "[.]"] <- gsub("^00", "0.0", x[!x %like% "[.]"])
     # remove last zeroes
-    x <- gsub("([.].?)0+$", "\\1", x, perl = TRUE)
-    x <- gsub("(.*[.])0+$", "\\10", x, perl = TRUE)
+    x <- gsub("([.].?)0+$", "\\1", x)
+    x <- gsub("(.*[.])0+$", "\\10", x)
     # remove ending .0 again
     x[x %like% "[.]"] <- gsub("0+$", "", x[x %like% "[.]"])
     # never end with dot
-    x <- gsub("[.]$", "", x, perl = TRUE)
+    x <- gsub("[.]$", "", x)
     # force to be character
     x <- as.character(x)
     # trim it
@@ -199,21 +161,21 @@ is.mic <- function(x) {
 #' @export
 #' @noRd
 as.double.mic <- function(x, ...) {
-  as.double(gsub("[<=>]+", "", as.character(x), perl = TRUE))
+  as.double(gsub("[<=>]+", "", as.character(x)))
 }
 
 #' @method as.integer mic
 #' @export
 #' @noRd
 as.integer.mic <- function(x, ...) {
-  as.integer(gsub("[<=>]+", "", as.character(x), perl = TRUE))
+  as.integer(gsub("[<=>]+", "", as.character(x)))
 }
 
 #' @method as.numeric mic
 #' @export
 #' @noRd
 as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub("[<=>]+", "", as.character(x), perl = TRUE))
+  as.numeric(gsub("[<=>]+", "", as.character(x)))
 }
 
 #' @method droplevels mic
@@ -235,7 +197,6 @@ 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)))
 }
 
@@ -250,24 +211,22 @@ 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, ...) {
-  summary(as.double(object), ...)
-}
-
-#' @method as.matrix mic
-#' @export
-#' @noRd
-as.matrix.mic <- function(x, ...) {
-  as.matrix(as.double(x), ...)
+  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
 }
 
 #' @method [ mic
@@ -322,50 +281,81 @@ unique.mic <- function(x, incomparables = FALSE, ...) {
   y
 }
 
-#' @method sort mic
+#' @method range mic
 #' @export
 #' @noRd
-sort.mic <- function(x, decreasing = FALSE, ...) {
-  if (decreasing == TRUE) {
-    ord <- order(-as.double(x))
-  } else {
-    ord <- order(as.double(x))
+range.mic <- function(..., na.rm = FALSE) {
+  rng <- sort(c(...))
+  if (na.rm == TRUE) {
+    rng <- rng[!is.na(rng)]
   }
-  x[ord]
+  out <- c(as.character(rng[1]), as.character(rng[length(rng)]))
+  as.double(as.mic(out))
 }
 
-#' @method hist mic
-#' @importFrom graphics hist
+#' @method min mic
 #' @export
 #' @noRd
-hist.mic <- function(x, ...) {
-  warning_("Use `plot()` or `ggplot()` for optimal plotting of MIC values", call = FALSE)
-  hist(log2(x))
+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])))
 }
 
-# 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 = ~stats::median(., na.rm = TRUE),
-    n_unique = ~pm_n_distinct(., na.rm = TRUE),
-    hist_log2 = ~skimr::inline_hist(log2(stats::na.omit(.)))
-  )
+#' @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)])))
 }
 
-# Miscellaneous mathematical functions ------------------------------------
+#' @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))
+}
 
 #' @method mean mic
 #' @export
 #' @noRd
-mean.mic <- function(x, trim = 0, na.rm = FALSE, ...) {
-  mean(as.double(x), trim = trim, na.rm = na.rm, ...)
+mean.mic <- function(x, na.rm = FALSE, ...) {
+  mean(as.double(x), na.rm = na.rm, ...)
 }
 
 #' @method median mic
-#' @importFrom stats median
 #' @export
 #' @noRd
 median.mic <- function(x, na.rm = FALSE, ...) {
@@ -373,241 +363,26 @@ median.mic <- function(x, na.rm = FALSE, ...) {
 }
 
 #' @method quantile mic
-#' @importFrom stats quantile
 #' @export
 #' @noRd
 quantile.mic <- function(x, probs = seq(0, 1, 0.25), na.rm = FALSE,
                          names = TRUE, type = 7, ...) {
-  quantile(as.double(x), probs = probs, na.rm = na.rm, names = names, type = type, ...)
+  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
@@ -658,27 +433,6 @@ cummin.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
@@ -721,47 +475,36 @@ cummin.mic <- function(x) {
   as.double(e1) > as.double(e2)
 }
 
-# Summary (see ?groupGeneric) -------------------------------------------
+#' @method sort 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]
+}
 
-#' @method all mic
+
+
+#' @method hist mic
 #' @export
 #' @noRd
-all.mic <- function(..., na.rm = FALSE) {
-  all(as.double(c(...)), na.rm = na.rm)
+hist.mic <- function(x, ...) {
+  warning_("Use `plot()` or `ggplot()` for plotting MIC values", call = FALSE)
+  hist(as.double(x), ...)
 }
-#' @method any mic
-#' @export
-#' @noRd
-any.mic <- function(..., na.rm = FALSE) {
-  any(as.double(c(...)), na.rm = na.rm)
-}
-#' @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)
+
+# 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(.))))
+  )
 }

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 Stable Lifecycle
+#' @inheritSection lifecycle Maturing 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

@@ -108,6 +108,7 @@ plot.mic <- function(x,
                                               fn = as.mic,
                                               language = language,
                                               ...)
+  
   barplot(x,
           col = cols_sub$cols,
           main = main,
@@ -115,7 +116,7 @@ plot.mic <- function(x,
           ylab = ylab,
           xlab = xlab,
           axes = FALSE)
-  axis(2, seq(0, max(x)))
+  axis(2, seq(0, max(as.double(x))))
   if (!is.null(cols_sub$sub)) {
     mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
   }
@@ -123,15 +124,15 @@ plot.mic <- function(x,
   if (any(colours_RSI %in% cols_sub$cols)) {
     legend_txt <- character(0)
     legend_col <- character(0)
-    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
+    if (colours_RSI[2] %in% cols_sub$cols) {
       legend_txt <- "Susceptible"
       legend_col <- colours_RSI[2]
     }
-    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
+    if (colours_RSI[3] %in% cols_sub$cols) {
       legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
       legend_col <- c(legend_col, colours_RSI[3])
     }
-    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
+    if (colours_RSI[1] %in% cols_sub$cols) {
       legend_txt <- c(legend_txt, "Resistant")
       legend_col <- c(legend_col, colours_RSI[1])
     }
@@ -316,15 +317,15 @@ plot.disk <- function(x,
   if (any(colours_RSI %in% cols_sub$cols)) {
     legend_txt <- character(0)
     legend_col <- character(0)
-    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
+    if (colours_RSI[1] %in% cols_sub$cols) {
       legend_txt <- "Resistant"
       legend_col <- colours_RSI[1]
     }
-    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
+    if (colours_RSI[3] %in% cols_sub$cols) {
       legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
       legend_col <- c(legend_col, colours_RSI[3])
     }
-    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
+    if (colours_RSI[2] %in% cols_sub$cols) {
       legend_txt <- c(legend_txt, "Susceptible")
       legend_col <- c(legend_col, colours_RSI[2])
     }
@@ -458,8 +459,8 @@ 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(x) / 2
-      while (min(extra_range) / 2 > min(x)) {
+      extra_range <- max(as.double(x)) / 2
+      while (min(extra_range) / 2 > min(as.double(x))) {
         extra_range <- c(min(extra_range) / 2, extra_range)
       }
       nms <- extra_range
@@ -524,9 +525,10 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_RSI, f
     cols <- "#BEBEBE"
     sub <- NULL
   }
-  list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
+  list(cols = cols, sub = sub, guideline = guideline)
 }
 
+
 #' @method plot rsi
 #' @export
 #' @importFrom graphics plot text axis

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 Stable Lifecycle 
+#' @inheritSection lifecycle Maturing 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()]
@@ -119,15 +119,7 @@ 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)]
     }
-    out <- as.mic(sample(set_range, size = size, replace = TRUE))
-    # 50% chance that lowest will get <= and highest will get >=
-    if (stats::runif(1) > 0.5) {
-      out[out == min(out)] <- paste0("<=", out[out == min(out)])
-    }
-    if (stats::runif(1) > 0.5) {
-      out[out == max(out)] <- paste0(">=", out[out == max(out)])
-    }
-    return(out)
+    return(as.mic(sample(set_range, size = size, replace = TRUE)))
   } 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 Stable Lifecycle
+#' @inheritSection lifecycle Maturing 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`

R/rsi.R

@@ -339,7 +339,7 @@ as.rsi.mic <- function(x,
   
   # for dplyr's across()
   cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
-  if (!is.null(cur_column_dplyr) && tryCatch(is.data.frame(get_current_data("ab", 0)), error = function(e) FALSE)) {
+  if (!is.null(cur_column_dplyr)) {
     # try to get current column, which will only be available when in across()
     ab <- tryCatch(cur_column_dplyr(),
                    error = function(e) ab)
@@ -428,7 +428,7 @@ as.rsi.disk <- function(x,
   
   # for dplyr's across()
   cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
-  if (!is.null(cur_column_dplyr) && tryCatch(is.data.frame(get_current_data("ab", 0)), error = function(e) FALSE)) {
+  if (!is.null(cur_column_dplyr)) {
     # try to get current column, which will only be available when in across()
     ab <- tryCatch(cur_column_dplyr(),
                    error = function(e) ab)
@@ -704,6 +704,7 @@ exec_as.rsi <- function(method,
                         conserve_capped_values, 
                         add_intrinsic_resistance,
                         reference_data) {
+  
   metadata_mo <- get_mo_failures_uncertainties_renamed()
   
   x_bak <- data.frame(x_mo = paste0(x, mo), stringsAsFactors = FALSE)
@@ -720,10 +721,10 @@ exec_as.rsi <- function(method,
   warned <- FALSE
   method_param <- toupper(method)
   
-  genera <- mo_genus(mo, language = NULL)
-  mo_genus <- as.mo(genera, language = NULL)
-  mo_family <- as.mo(mo_family(mo, language = NULL))
-  mo_order <- as.mo(mo_order(mo, language = NULL))
+  genera <- mo_genus(mo)
+  mo_genus <- as.mo(genera)
+  mo_family <- as.mo(mo_family(mo))
+  mo_order <- as.mo(mo_order(mo))
   if (any(genera == "Staphylococcus", na.rm = TRUE)) {
     mo_becker <- as.mo(mo, Becker = TRUE)
   } else {
@@ -807,19 +808,23 @@ exec_as.rsi <- function(method,
         pm_filter(uti == FALSE) %pm>% # 'uti' is a column in rsi_translation
         pm_arrange(pm_desc(nchar(mo)))
     }
-    
     get_record <- get_record[1L, , drop = FALSE]
     
     if (NROW(get_record) > 0) {
-      if (is.na(x[i]) | (is.na(get_record$breakpoint_S) & is.na(get_record$breakpoint_R))) {
+      if (is.na(x[i])) {
         new_rsi[i] <- NA_character_
       } else if (method == "mic") {
-        new_rsi[i] <- quick_case_when(isTRUE(conserve_capped_values) & x[i] %like% "^<[0-9]" ~ "S",
-                                      isTRUE(conserve_capped_values) & x[i] %like% "^>[0-9]" ~ "R",
+        mic_input <- x[i]
+        mic_S <- as.mic(get_record$breakpoint_S)
+        mic_R <- as.mic(get_record$breakpoint_R)
+        new_rsi[i] <- quick_case_when(isTRUE(conserve_capped_values) & mic_input %like% "^<[0-9]" ~ "S",
+                                      isTRUE(conserve_capped_values) & mic_input %like% "^>[0-9]" ~ "R",
                                       # start interpreting: EUCAST uses <= S and > R, CLSI uses <=S and >= R
-                                      x[i] <= get_record$breakpoint_S ~ "S",
-                                      guideline_coerced %like% "EUCAST" & x[i] > get_record$breakpoint_R ~ "R",
-                                      guideline_coerced %like% "CLSI" & x[i] >= get_record$breakpoint_R ~ "R",
+                                      isTRUE(which(levels(mic_input) == mic_input) <= which(levels(mic_S) == mic_S)) ~ "S",
+                                      guideline_coerced %like% "EUCAST" &
+                                        isTRUE(which(levels(mic_input) == mic_input) > which(levels(mic_R) == mic_R)) ~ "R",
+                                      guideline_coerced %like% "CLSI" &
+                                        isTRUE(which(levels(mic_input) == mic_input) >= which(levels(mic_R) == mic_R)) ~ "R",
                                       # return "I" when not match the bottom or top
                                       !is.na(get_record$breakpoint_S) & !is.na(get_record$breakpoint_R) ~ "I",
                                       # and NA otherwise

data-raw/translations.tsv

@@ -33,7 +33,7 @@ no .*growth	TRUE	FALSE	FALSE	keine? .*wachstum	geen .*groei	no .*crecimientonon
 no|not	TRUE	FALSE	FALSE	keine?	geen|niet	no|sin	sem	non	sem
 Susceptible	TRUE	FALSE	FALSE	Empfindlich	Gevoelig	Susceptible			
 Intermediate	TRUE	FALSE	FALSE	Mittlere	Intermediair	Intermedio			
-Incr. exposure	TRUE	FALSE	FALSE	Empfindlich, erh Belastung	'Incr. exposure'	'Incr. exposure'			
+Incr. exposure	TRUE	FALSE	FALSE	Empfindlich, erh Belastung	Gevoelig, 'incr. exposure'	Susceptible, 'incr. exposure'			
 Resistant	TRUE	FALSE	FALSE	Resistent	Resistent	Resistente			
 antibiotic	TRUE	TRUE	FALSE	Antibiotikum	antibioticum	antibiótico			
 Antibiotic	TRUE	TRUE	FALSE	Antibiotikum	Antibioticum	Antibiótico			

docs/404.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/LICENSE-text.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/articles/index.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/authors.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/index.html

@@ -43,7 +43,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/news/index.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 
@@ -236,13 +236,13 @@
       <small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
     </div>
 
-    <div id="amr-1509039" class="section level1">
-<h1 class="page-header" data-toc-text="1.5.0.9039">
-<a href="#amr-1509039" class="anchor"></a>AMR 1.5.0.9039<small> Unreleased </small>
+    <div id="amr-1509031" class="section level1">
+<h1 class="page-header" data-toc-text="1.5.0.9031">
+<a href="#amr-1509031" class="anchor"></a>AMR 1.5.0.9031<small> Unreleased </small>
 </h1>
-<div id="last-updated-8-march-2021" class="section level2">
+<div id="last-updated-5-march-2021" class="section level2">
 <h2 class="hasAnchor">
-<a href="#last-updated-8-march-2021" class="anchor"></a><small>Last updated: 8 March 2021</small>
+<a href="#last-updated-5-march-2021" class="anchor"></a><small>Last updated: 5 March 2021</small>
 </h2>
 <div id="new" class="section level3">
 <h3 class="hasAnchor">
@@ -280,7 +280,7 @@
 </li>
 <li><p>Support for custom MDRO guidelines, using the new <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function, please see <code><a href="../reference/mdro.html">mdro()</a></code> for additional info</p></li>
 <li><p>Function <code><a href="../reference/isolate_identifier.html">isolate_identifier()</a></code>, which will paste a microorganism code with all antimicrobial results of a data set into one string for each row. This is useful to compare isolates, e.g. between institutions or regions, when there is no genotyping available.</p></li>
-<li><p><code>ggplot()</code> generics for classes <code>&lt;mic&gt;</code> and <code>&lt;disk&gt;</code></p></li>
+<li><p><code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> generics for classes <code>&lt;mic&gt;</code> and <code>&lt;disk&gt;</code></p></li>
 <li>
 <p>Function <code><a href="../reference/mo_property.html">mo_is_yeast()</a></code>, which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
@@ -321,7 +321,7 @@
 <li>Plotting of MIC and disk diffusion values now support interpretation colouring if you supply the microorganism and antimicrobial agent</li>
 <li>All colours were updated to colour-blind friendly versions for values R, S and I for all plot methods (also applies to tibble printing)</li>
 <li>Interpretation of MIC and disk diffusion values to R/SI will now be translated if the system language is German, Dutch or Spanish (see <code>translate</code>)</li>
-<li>Plotting is now possible with base R using <code><a href="../reference/plot.html">plot()</a></code> and with ggplot2 using <code>ggplot()</code> on any vector of MIC and disk diffusion values</li>
+<li>Plotting is now possible with base R using <code><a href="../reference/plot.html">plot()</a></code> and with ggplot2 using <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> on any vector of MIC and disk diffusion values</li>
 </ul>
 </li>
 <li>
@@ -351,7 +351,6 @@
 </li>
 <li>Added translations of German and Spanish for more than 200 antimicrobial drugs</li>
 <li>Speed improvement for <code><a href="../reference/as.ab.html">as.ab()</a></code> when the input is an official name or ATC code</li>
-<li>Added argument <code>include_untested_rsi</code> to the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> functions (defaults to <code>TRUE</code> to keep existing behaviour), to be able to exclude rows where all R/SI values (class <code>&lt;rsi&gt;</code>, see <code><a href="../reference/as.rsi.html">as.rsi()</a></code>) are empty</li>
 </ul>
 </div>
 <div id="other" class="section level3">

docs/pkgdown.yml

@@ -12,7 +12,7 @@ articles:
   datasets: datasets.html
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
-last_built: 2021-03-08T01:35Z
+last_built: 2021-03-05T14:27Z
 urls:
   reference: https://msberends.github.io/AMR//reference
   article: https://msberends.github.io/AMR//articles

docs/reference/AMR.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/reference/ab_from_text.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
       </span>
     </div>
 
@@ -301,13 +301,12 @@
 <p>With using <code>collapse</code>, this function will return a <a href='https://rdrr.io/r/base/character.html'>character</a>:<br />
 <code>df %&gt;% mutate(abx = ab_from_text(clinical_text, collapse = "|"))</code></p>
 
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
     <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>
 
     

docs/reference/first_isolate.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9019</span>
       </span>
     </div>
 
@@ -260,7 +260,6 @@
   points_threshold <span class='op'>=</span> <span class='fl'>2</span>,
   info <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/interactive.html'>interactive</a></span><span class='op'>(</span><span class='op'>)</span>,
   include_unknown <span class='op'>=</span> <span class='cn'>FALSE</span>,
-  include_untested_rsi <span class='op'>=</span> <span class='cn'>TRUE</span>,
   <span class='va'>...</span>
 <span class='op'>)</span>
 
@@ -326,7 +325,7 @@
     </tr>
     <tr>
       <th>icu_exclude</th>
-      <td><p>logical to indicate whether ICU isolates should be excluded (rows with value <code>TRUE</code> in the column set with <code>col_icu</code>)</p></td>
+      <td><p>logical whether ICU isolates should be excluded (rows with value <code>TRUE</code> in the column set with <code>col_icu</code>)</p></td>
     </tr>
     <tr>
       <th>specimen_group</th>
@@ -338,7 +337,7 @@
     </tr>
     <tr>
       <th>ignore_I</th>
-      <td><p>logical to indicate whether antibiotic interpretations with <code>"I"</code> will be ignored when <code>type = "keyantibiotics"</code>, see <em>Details</em></p></td>
+      <td><p>logical to determine whether antibiotic interpretations with <code>"I"</code> will be ignored when <code>type = "keyantibiotics"</code>, see <em>Details</em></p></td>
     </tr>
     <tr>
       <th>points_threshold</th>
@@ -350,11 +349,7 @@
     </tr>
     <tr>
       <th>include_unknown</th>
-      <td><p>logical to indicate whether 'unknown' microorganisms should be included too, i.e. microbial code <code>"UNKNOWN"</code>, which defaults to <code>FALSE</code>. For WHONET users, this means that all records with organism code <code>"con"</code> (<em>contamination</em>) will be excluded at default. Isolates with a microbial ID of <code>NA</code> will always be excluded as first isolate.</p></td>
-    </tr>
-    <tr>
-      <th>include_untested_rsi</th>
-      <td><p>logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use <code>include_untested_rsi = FALSE</code> to always return <code>FALSE</code> for such rows. This checks the data set for columns of class <code>&lt;rsi&gt;</code> and consequently requires transforming columns with antibiotic results using <code><a href='as.rsi.html'>as.rsi()</a></code> first.</p></td>
+      <td><p>logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code <code>"UNKNOWN"</code>, which defaults to <code>FALSE</code>. For WHONET users, this means that all records with organism code <code>"con"</code> (<em>contamination</em>) will be excluded at default. Isolates with a microbial ID of <code>NA</code> will always be excluded as first isolate.</p></td>
     </tr>
     <tr>
       <th>...</th>

docs/reference/ggplot_pca.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9013</span>
       </span>
     </div>
 
@@ -384,13 +384,12 @@
     <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
 
     <p>The colours for labels and points can be changed by adding another scale layer for colour, like <code><a href='https://ggplot2.tidyverse.org/reference/scale_viridis.html'>scale_colour_viridis_d()</a></code> or <code><a href='https://ggplot2.tidyverse.org/reference/scale_brewer.html'>scale_colour_brewer()</a></code>.</p>
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><span class='co'># `example_isolates` is a data set available in the AMR package.</span>

docs/reference/ggplot_rsi.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 
@@ -415,13 +415,12 @@
 <p><code>labels_rsi_count()</code> print datalabels on the bars with percentage and amount of isolates using <code><a href='https://ggplot2.tidyverse.org/reference/geom_text.html'>ggplot2::geom_text()</a></code>.</p>
 <p><code>ggplot_rsi()</code> is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (<code>%&gt;%</code>). See <em>Examples</em>.</p>
 
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
     <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>
 
     

docs/reference/index.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

docs/reference/key_antibiotics.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9019</span>
       </span>
     </div>
 
@@ -317,7 +317,7 @@
     </tr>
     <tr>
       <th>ignore_I</th>
-      <td><p>logical to indicate whether antibiotic interpretations with <code>"I"</code> will be ignored when <code>type = "keyantibiotics"</code>, see <em>Details</em></p></td>
+      <td><p>logical to determine whether antibiotic interpretations with <code>"I"</code> will be ignored when <code>type = "keyantibiotics"</code>, see <em>Details</em></p></td>
     </tr>
     <tr>
       <th>points_threshold</th>

docs/reference/pca.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9016</span>
       </span>
     </div>
 
@@ -311,13 +311,12 @@
 
     <p>The <code>pca()</code> function takes a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> as input and performs the actual PCA with the <span style="R">R</span> function <code><a href='https://rdrr.io/r/stats/prcomp.html'>prcomp()</a></code>.</p>
 <p>The result of the <code>pca()</code> function is a <a href='https://rdrr.io/r/stats/prcomp.html'>prcomp</a> object, with an additional attribute <code>non_numeric_cols</code> which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by <code><a href='ggplot_pca.html'>ggplot_pca()</a></code>.</p>
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
     <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>
 
     

docs/reference/random.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 
@@ -280,13 +280,12 @@
 
     <p>The base R function <code><a href='https://rdrr.io/r/base/sample.html'>sample()</a></code> is used for generating values.</p>
 <p>Generated values are based on the latest EUCAST guideline implemented in the <a href='rsi_translation.html'>rsi_translation</a> data set. To create specific generated values per bug or drug, set the <code>mo</code> and/or <code>ab</code> argument.</p>
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
     <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on Our Website!</h2>
 
     

docs/reference/resistance_predict.html

@@ -82,7 +82,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="../index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9019</span>
       </span>
     </div>
 
@@ -364,13 +364,12 @@
 <li><p><code>"lin"</code> or <code>"linear"</code>: a linear regression model</p></li>
 </ul>
 
-    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
+    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
 
     
 
-<p><img src='figures/lifecycle_stable.svg' style=margin-bottom:5px /> <br />
-The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</strong>. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.</p>
-<p>If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.</p>
+<p><img src='figures/lifecycle_maturing.svg' style=margin-bottom:5px /> <br />
+The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing</strong>. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome <a href='https://github.com/msberends/AMR/issues'>to suggest changes at our repository</a> or <a href='AMR.html'>write us an email (see section 'Contact Us')</a>.</p>
     <h2 class="hasAnchor" id="interpretation-of-r-and-s-i"><a class="anchor" href="#interpretation-of-r-and-s-i"></a>Interpretation of R and S/I</h2>
 
     

docs/survey.html

@@ -81,7 +81,7 @@
       </button>
       <span class="navbar-brand">
         <a class="navbar-link" href="index.html">AMR (for R)</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9039</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9031</span>
       </span>
     </div>
 

man/ab_from_text.Rd

@@ -54,12 +54,10 @@ With using \code{collapse}, this function will return a \link{character}:\cr
 \code{df \%>\% mutate(abx = ab_from_text(clinical_text, collapse = "|"))}
 }
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \section{Read more on Our Website!}{

man/as.mic.Rd

@@ -11,50 +11,18 @@ as.mic(x, na.rm = FALSE)
 is.mic(x)
 }
 \arguments{
-\item{x}{character or numeric vector}
+\item{x}{vector}
 
 \item{na.rm}{a logical indicating whether missing values should be removed}
 }
 \value{
-Ordered \link{factor} with additional class \code{\link{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.
+Ordered \link{factor} with additional class \code{\link{mic}}
 }
 \description{
-This ransforms vectors to a new class \code{\link{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.
+This transforms a vector to a new class \code{\link{mic}}, which is an ordered \link{factor} with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as \code{NA} with a warning.
 }
 \details{
 To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST and CLSI.
-
-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:\preformatted{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.:\preformatted{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 \link[=groupGeneric]{generic functions} are implemented for the MIC class: \code{!}, \code{!=}, \code{\%\%}, \code{\%/\%}, \code{&}, \code{*}, \code{+}, \code{-}, \code{/}, \code{<}, \code{<=}, \code{==}, \code{>}, \code{>=}, \code{^}, \code{|}, \code{\link[=abs]{abs()}}, \code{\link[=acos]{acos()}}, \code{\link[=acosh]{acosh()}}, \code{\link[=all]{all()}}, \code{\link[=any]{any()}}, \code{\link[=asin]{asin()}}, \code{\link[=asinh]{asinh()}}, \code{\link[=atan]{atan()}}, \code{\link[=atanh]{atanh()}}, \code{\link[=ceiling]{ceiling()}}, \code{\link[=cos]{cos()}}, \code{\link[=cosh]{cosh()}}, \code{\link[=cospi]{cospi()}}, \code{\link[=cummax]{cummax()}}, \code{\link[=cummin]{cummin()}}, \code{\link[=cumprod]{cumprod()}}, \code{\link[=cumsum]{cumsum()}}, \code{\link[=digamma]{digamma()}}, \code{\link[=exp]{exp()}}, \code{\link[=expm1]{expm1()}}, \code{\link[=floor]{floor()}}, \code{\link[=gamma]{gamma()}}, \code{\link[=lgamma]{lgamma()}}, \code{\link[=log]{log()}}, \code{\link[=log1p]{log1p()}}, \code{\link[=log2]{log2()}}, \code{\link[=log10]{log10()}}, \code{\link[=max]{max()}}, \code{\link[=mean]{mean()}}, \code{\link[=min]{min()}}, \code{\link[=prod]{prod()}}, \code{\link[=range]{range()}}, \code{\link[=round]{round()}}, \code{\link[=sign]{sign()}}, \code{\link[=signif]{signif()}}, \code{\link[=sin]{sin()}}, \code{\link[=sinh]{sinh()}}, \code{\link[=sinpi]{sinpi()}}, \code{\link[=sqrt]{sqrt()}}, \code{\link[=sum]{sum()}}, \code{\link[=tan]{tan()}}, \code{\link[=tanh]{tanh()}}, \code{\link[=tanpi]{tanpi()}}, \code{\link[=trigamma]{trigamma()}} and \code{\link[=trunc]{trunc()}}. Some functions of the \code{stats} package are also implemented: \code{\link[=median]{median()}}, \code{\link[=quantile]{quantile()}}, \code{\link[=mad]{mad()}}, \code{\link[=IQR]{IQR()}}, \code{\link[=fivenum]{fivenum()}}. Also, \code{\link[=boxplot.stats]{boxplot.stats()}} is supported. Since \code{\link[=sd]{sd()}} and \code{\link[=var]{var()}} are non-generic functions, these could not be extended. Use \code{\link[=mad]{mad()}} as an alternative, or use e.g. \code{sd(as.numeric(x))} where \code{x} is your vector of MIC values.
 }
 \section{Stable Lifecycle}{
 

man/first_isolate.Rd

@@ -29,7 +29,6 @@ first_isolate(
   points_threshold = 2,
   info = interactive(),
   include_unknown = FALSE,
-  include_untested_rsi = TRUE,
   ...
 )
 
@@ -71,21 +70,19 @@ filter_first_weighted_isolate(
 
 \item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
 
-\item{icu_exclude}{logical to indicate whether ICU isolates should be excluded (rows with value \code{TRUE} in the column set with \code{col_icu})}
+\item{icu_exclude}{logical whether ICU isolates should be excluded (rows with value \code{TRUE} in the column set with \code{col_icu})}
 
 \item{specimen_group}{value in the column set with \code{col_specimen} to filter on}
 
 \item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see \emph{Details}}
 
-\item{ignore_I}{logical to indicate whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see \emph{Details}}
+\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see \emph{Details}}
 
 \item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see \emph{Details}}
 
 \item{info}{print progress}
 
-\item{include_unknown}{logical to indicate whether 'unknown' microorganisms should be included too, i.e. microbial code \code{"UNKNOWN"}, which defaults to \code{FALSE}. For WHONET users, this means that all records with organism code \code{"con"} (\emph{contamination}) will be excluded at default. Isolates with a microbial ID of \code{NA} will always be excluded as first isolate.}
-
-\item{include_untested_rsi}{logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use \code{include_untested_rsi = FALSE} to always return \code{FALSE} for such rows. This checks the data set for columns of class \verb{<rsi>} and consequently requires transforming columns with antibiotic results using \code{\link[=as.rsi]{as.rsi()}} first.}
+\item{include_unknown}{logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code \code{"UNKNOWN"}, which defaults to \code{FALSE}. For WHONET users, this means that all records with organism code \code{"con"} (\emph{contamination}) will be excluded at default. Isolates with a microbial ID of \code{NA} will always be excluded as first isolate.}
 
 \item{...}{arguments passed on to \code{\link[=first_isolate]{first_isolate()}} when using \code{\link[=filter_first_isolate]{filter_first_isolate()}}, or arguments passed on to \code{\link[=key_antibiotics]{key_antibiotics()}} when using \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}}}
 }

man/ggplot_pca.Rd

@@ -108,12 +108,10 @@ Produces a \code{ggplot2} variant of a so-called \href{https://en.wikipedia.org/
 \details{
 The colours for labels and points can be changed by adding another scale layer for colour, like \code{scale_colour_viridis_d()} or \code{scale_colour_brewer()}.
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \examples{

man/ggplot_rsi.Rd

@@ -140,12 +140,10 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
 \code{\link[=ggplot_rsi]{ggplot_rsi()}} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\verb{\%>\%}). See \emph{Examples}.
 }
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \section{Read more on Our Website!}{

man/key_antibiotics.Rd

@@ -58,7 +58,7 @@ key_antibiotics_equal(
 
 \item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see \emph{Details}}
 
-\item{ignore_I}{logical to indicate whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see \emph{Details}}
+\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see \emph{Details}}
 
 \item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see \emph{Details}}
 

man/pca.Rd

@@ -59,12 +59,10 @@ The \code{\link[=pca]{pca()}} function takes a \link{data.frame} as input and pe
 
 The result of the \code{\link[=pca]{pca()}} function is a \link{prcomp} object, with an additional attribute \code{non_numeric_cols} which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by \code{\link[=ggplot_pca]{ggplot_pca()}}.
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \section{Read more on Our Website!}{

man/random.Rd

@@ -35,12 +35,10 @@ The base R function \code{\link[=sample]{sample()}} is used for generating value
 
 Generated values are based on the latest EUCAST guideline implemented in the \link{rsi_translation} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument.
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \section{Read more on Our Website!}{

man/resistance_predict.Rd

@@ -100,12 +100,10 @@ Valid options for the statistical model (argument \code{model}) are:
 \item \code{"lin"} or \code{"linear"}: a linear regression model
 }
 }
-\section{Stable Lifecycle}{
+\section{Maturing Lifecycle}{
 
-\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
-The \link[=lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
-
-If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
+The \link[=lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome \href{https://github.com/msberends/AMR/issues}{to suggest changes at our repository} or \link[=AMR]{write us an email (see section 'Contact Us')}.
 }
 
 \section{Interpretation of R and S/I}{

tests/testthat/test-first_isolate.R

@@ -196,10 +196,6 @@ test_that("first isolates work", {
   expect_equal(sum(first_isolate(test_unknown)),
                1045)
   
-  # empty rsi results
-  expect_equal(sum(first_isolate(example_isolates, include_untested_rsi = FALSE)),
-               1287)
-  
   # shortcuts
   expect_identical(filter_first_isolate(example_isolates),
                    subset(example_isolates, first_isolate(example_isolates)))

tests/testthat/test-mic.R

@@ -31,12 +31,12 @@ test_that("mic works", {
   expect_true(as.mic("1") > as.mic("<=0.0625"))
   expect_true(as.mic("1") < as.mic(">=32"))
   expect_true(is.mic(as.mic(8)))
-  
+
   expect_equal(as.double(as.mic(">=32")), 32)
   expect_equal(as.numeric(as.mic(">=32")), 32)
   expect_equal(as.integer(as.mic(">=32")), 32)
   expect_equal(suppressWarnings(as.logical(as.mic("INVALID VALUE"))), NA)
-  
+
   # all levels should be valid MICs
   x <- as.mic(c(2, 4))
   expect_s3_class(x[1], "mic")
@@ -60,83 +60,12 @@ test_that("mic works", {
   }
   expect_output(print(as.mic(c(1, 2, 4, 8))))
   
-  expect_s3_class(summary(as.mic(c(2, 8))), c("summaryDefault", "table"))
+  expect_equal(summary(as.mic(c(2, 8))), 
+               structure(c("Class" = "mic",
+                           "<NA>" = "0",
+                           "Min." = "2",
+                           "Max." = "8"), class = c("summaryDefault", "table")))
   
   library(dplyr, warn.conflicts = FALSE)
   expect_output(print(tibble(m = as.mic(2:4))))
 })
-
-test_that("mathematical functions on mic work", {
-  skip_on_cran()
-  x <- random_mic(50)
-  x_double <- as.double(gsub("[<=>]+", "", as.character(x)))
-  suppressWarnings(expect_identical(mean(x), mean(x_double)))
-  suppressWarnings(expect_identical(median(x), median(x_double)))
-  suppressWarnings(expect_identical(quantile(x), quantile(x_double)))
-  suppressWarnings(expect_identical(abs(x), abs(x_double)))
-  suppressWarnings(expect_identical(sign(x), sign(x_double)))
-  suppressWarnings(expect_identical(sqrt(x), sqrt(x_double)))
-  suppressWarnings(expect_identical(floor(x), floor(x_double)))
-  suppressWarnings(expect_identical(ceiling(x), ceiling(x_double)))
-  suppressWarnings(expect_identical(trunc(x), trunc(x_double)))
-  suppressWarnings(expect_identical(round(x), round(x_double)))
-  suppressWarnings(expect_identical(signif(x), signif(x_double)))
-  suppressWarnings(expect_identical(exp(x), exp(x_double)))
-  suppressWarnings(expect_identical(log(x), log(x_double)))
-  suppressWarnings(expect_identical(log10(x), log10(x_double)))
-  suppressWarnings(expect_identical(log2(x), log2(x_double)))
-  suppressWarnings(expect_identical(expm1(x), expm1(x_double)))
-  suppressWarnings(expect_identical(log1p(x), log1p(x_double)))
-  suppressWarnings(expect_identical(cos(x), cos(x_double)))
-  suppressWarnings(expect_identical(sin(x), sin(x_double)))
-  suppressWarnings(expect_identical(tan(x), tan(x_double)))
-  suppressWarnings(expect_identical(cospi(x), cospi(x_double)))
-  suppressWarnings(expect_identical(sinpi(x), sinpi(x_double)))
-  suppressWarnings(expect_identical(tanpi(x), tanpi(x_double)))
-  suppressWarnings(expect_identical(acos(x), acos(x_double)))
-  suppressWarnings(expect_identical(asin(x), asin(x_double)))
-  suppressWarnings(expect_identical(atan(x), atan(x_double)))
-  suppressWarnings(expect_identical(cosh(x), cosh(x_double)))
-  suppressWarnings(expect_identical(sinh(x), sinh(x_double)))
-  suppressWarnings(expect_identical(tanh(x), tanh(x_double)))
-  suppressWarnings(expect_identical(acosh(x), acosh(x_double)))
-  suppressWarnings(expect_identical(asinh(x), asinh(x_double)))
-  suppressWarnings(expect_identical(atanh(x), atanh(x_double)))
-  suppressWarnings(expect_identical(lgamma(x), lgamma(x_double)))
-  suppressWarnings(expect_identical(gamma(x), gamma(x_double)))
-  suppressWarnings(expect_identical(digamma(x), digamma(x_double)))
-  suppressWarnings(expect_identical(trigamma(x), trigamma(x_double)))
-  suppressWarnings(expect_identical(cumsum(x), cumsum(x_double)))
-  suppressWarnings(expect_identical(cumprod(x), cumprod(x_double)))
-  suppressWarnings(expect_identical(cummax(x), cummax(x_double)))
-  suppressWarnings(expect_identical(cummin(x), cummin(x_double)))
-  suppressWarnings(expect_identical(!x, !(x_double)))
-  
-  suppressWarnings(expect_identical(all(x), all(x_double)))
-  suppressWarnings(expect_identical(any(x), any(x_double)))
-  suppressWarnings(expect_identical(sum(x), sum(x_double)))
-  suppressWarnings(expect_identical(prod(x), prod(x_double)))
-  suppressWarnings(expect_identical(min(x), min(x_double)))
-  suppressWarnings(expect_identical(max(x), max(x_double)))
-  suppressWarnings(expect_identical(range(x), range(x_double)))
-  
-  el1 <- random_mic(50)
-  el1_double <- as.double(gsub("[<=>]+", "", as.character(el1)))
-  el2 <- random_mic(50)
-  el2_double <- as.double(gsub("[<=>]+", "", as.character(el2)))
-  suppressWarnings(expect_identical(el1 + el2, el1_double + el2_double))
-  suppressWarnings(expect_identical(el1 - el2, el1_double - el2_double))
-  suppressWarnings(expect_identical(el1 * el2, el1_double * el2_double))
-  suppressWarnings(expect_identical(el1 / el2, el1_double / el2_double))
-  suppressWarnings(expect_identical(el1 ^ el2, el1_double ^ el2_double))
-  suppressWarnings(expect_identical(el1 %% el2, el1_double %% el2_double))
-  suppressWarnings(expect_identical(el1 %/% el2, el1_double %/% el2_double))
-  suppressWarnings(expect_identical(el1 & el2, el1_double & el2_double))
-  suppressWarnings(expect_identical(el1 | el2, el1_double | el2_double))
-  suppressWarnings(expect_identical(el1 == el2, el1_double == el2_double))
-  suppressWarnings(expect_identical(el1 != el2, el1_double != el2_double))
-  suppressWarnings(expect_identical(el1 < el2, el1_double < el2_double))
-  suppressWarnings(expect_identical(el1 <= el2, el1_double <= el2_double))
-  suppressWarnings(expect_identical(el1 >= el2, el1_double >= el2_double))
-  suppressWarnings(expect_identical(el1 > el2, el1_double > el2_double))
-})