v1.6.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.5.0.9031
-Date: 2021-03-05
+Version: 1.6.0
+Date: 2021-03-14
 Title: Antimicrobial Resistance Data Analysis
 Authors@R: c(
     person(role = c("aut", "cre"), 

NAMESPACE

@@ -1,8 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method("!",mic)
 S3method("!=",mic)
 S3method("%%",mic)
 S3method("%/%",mic)
+S3method("&",mic)
 S3method("*",mic)
 S3method("+",mic)
 S3method("-",mic)
@@ -14,57 +16,76 @@ S3method(">",mic)
 S3method(">=",mic)
 S3method("[",ab)
 S3method("[",disk)
-S3method("[",isolate_identifier)
 S3method("[",mic)
 S3method("[",mo)
 S3method("[<-",ab)
 S3method("[<-",disk)
-S3method("[<-",isolate_identifier)
 S3method("[<-",mic)
 S3method("[<-",mo)
 S3method("[<-",rsi)
 S3method("[[",ab)
 S3method("[[",disk)
-S3method("[[",isolate_identifier)
 S3method("[[",mic)
 S3method("[[",mo)
 S3method("[[<-",ab)
 S3method("[[<-",disk)
-S3method("[[<-",isolate_identifier)
 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)
 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)
 S3method(c,ab)
 S3method(c,disk)
-S3method(c,isolate_identifier)
 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)
@@ -78,28 +99,39 @@ S3method(print,bug_drug_combinations)
 S3method(print,catalogue_of_life_version)
 S3method(print,custom_mdro_guideline)
 S3method(print,disk)
-S3method(print,isolate_identifier)
 S3method(print,mic)
 S3method(print,mo)
 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)
 S3method(unique,mic)
 S3method(unique,mo)
 S3method(unique,rsi)
@@ -195,7 +227,6 @@ export(is.mo)
 export(is.rsi)
 export(is.rsi.eligible)
 export(is_new_episode)
-export(isolate_identifier)
 export(key_antibiotics)
 export(key_antibiotics_equal)
 export(kurtosis)
@@ -269,6 +300,7 @@ export(theme_rsi)
 importFrom(graphics,arrows)
 importFrom(graphics,axis)
 importFrom(graphics,barplot)
+importFrom(graphics,hist)
 importFrom(graphics,legend)
 importFrom(graphics,mtext)
 importFrom(graphics,plot)
@@ -277,8 +309,10 @@ 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 1.5.0.9031
-## <small>Last updated:  5 March 2021</small>
+# AMR 1.6.0
+
 
 ### 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.
@@ -22,7 +22,6 @@
   #> Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either "R", "S" or "I"
    ```
 * Support for custom MDRO guidelines, using the new `custom_mdro_guideline()` function, please see `mdro()` for additional info
-* Function `isolate_identifier()`, 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.
 * `ggplot()` generics for classes `<mic>` and `<disk>`
 * Function `mo_is_yeast()`, which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:
   ```r
@@ -44,6 +43,20 @@
   #> [1] "Hongos"    "Levaduras"
   ```
 * Added Pretomanid (PMD, J04AK08) to the `antibiotics` data set
+* MIC values (see `as.mic()`) can now be used in any mathematical processing, such as usage inside functions `min()`, `max()`, `range()`, and with binary operators (`+`, `-`, etc.). This allows for easy distribution analysis and fast filtering on MIC values:
+  ```r
+  x <- random_mic(10)
+  x
+  #> Class <mic>
+  #>  [1] 128   0.5   2     0.125 64    0.25  >=256 8     16    4
+  x[x > 4]
+  #> Class <mic>
+  #> [1] 128   64    >=256 8     16
+  range(x)
+  #> [1]   0.125 256.000
+  range(log2(x))
+  #> [1] -3  8
+  ```
 
 ### Changed
 * Updated the bacterial taxonomy to 3 March 2021 (using [LSPN](https://lpsn.dsmz.de))
@@ -54,10 +67,10 @@
   * All colours were updated to colour-blind friendly versions for values R, S and I for all plot methods (also applies to tibble printing)
   * Interpretation of MIC and disk diffusion values to R/SI will now be translated if the system language is German, Dutch or Spanish (see `translate`)
   * Plotting is now possible with base R using `plot()` and with ggplot2 using `ggplot()` on any vector of MIC and disk diffusion values
+* Updated SNOMED codes to US Edition of SNOMED CT from 1 September 2020 and added the source to the help page of the `microorganisms` data set
 * `is.rsi()` and `is.rsi.eligible()` now return a vector of `TRUE`/`FALSE` when the input is a data set, by iterating over all columns
 * Using functions without setting a data set (e.g., `mo_is_gram_negative()`, `mo_is_gram_positive()`, `mo_is_intrinsic_resistant()`, `first_isolate()`, `mdro()`) now work with `dplyr`s `group_by()` again
 * `first_isolate()` can be used with `group_by()` (also when using a dot `.` as input for the data) and now returns the names of the groups
-* MIC values now allow for any mathematical processing, such as usage inside functions `min()`, `max()`, `range()`, and with binary operators (+, -, etc.). This also enables other functions, such as `fivenum()`.
 * Updated the data set `microorganisms.codes` (which contains popular LIS and WHONET codes for microorganisms) for some species of *Mycobacterium* that previously incorrectly returned *M. africanum*
 * WHONET code `"PNV"` will now correctly be interpreted as `PHN`, the antibiotic code for phenoxymethylpenicillin ('peni V')
 * Fix for verbose output of `mdro(..., verbose = TRUE)` for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only *P. aeruginosa*)
@@ -73,6 +86,7 @@
 * 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
@@ -393,7 +407,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
   * Speed improvements, especially for the *G. species* format (G for genus), like *E. coli* and *K pneumoniae*
   * Support for more common codes used in laboratory information systems
 * Input values for `as.disk()` limited to a maximum of 50 millimeters
-* Added a lifecycle state to every function, following [the lifecycle circle of the `tidyverse`](https://www.tidyverse.org/lifecycle)
+* Added a lifecycle state to every function, following the lifecycle circle of the `tidyverse`
 * For in `as.ab()`: support for drugs starting with "co-" like co-amoxiclav, co-trimoxazole, co-trimazine and co-trimazole (thanks to Peter Dutey)
 * Changes to the `antibiotics` data set (thanks to Peter Dutey):
   * Added more synonyms to colistin, imipenem and piperacillin/tazobactam

R/aa_helper_functions.R

@@ -507,12 +507,12 @@ format_class <- function(class, plural) {
   if ("isolate_identifier" %in% class) {
     class <- "created with isolate_identifier()"
   }
-  if (any(c("mo", "ab", "rsi", "disk", "mic") %in% class)) {
+  if (any(c("mo", "ab", "rsi") %in% class)) {
     class <- paste0("of class <", class[1L], ">")
   }
   class[class == class.bak] <- paste0("of class <", class[class == class.bak], ">")
   # output
-  vector_or(class, quotes = FALSE)
+  vector_or(class, quotes = FALSE, sort = FALSE)
 }
 
 # a check for every single argument in all functions
@@ -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 <- import_fn("new_pillar_shaft_simple", "pillar")
   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

@@ -105,7 +105,6 @@ 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_class_selectors.R

@@ -27,7 +27,7 @@
 #' 
 #' These functions help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. \strong{\Sexpr{ifelse(as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2, paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}
 #' @inheritSection lifecycle Stable Lifecycle
-#' @param only_rsi_columns a logical to indicate whether only columns of class [`<rsi>`]([rsi]) must be selected (defaults to `FALSE`)
+#' @param only_rsi_columns a logical to indicate whether only columns of class `<rsi>` must be selected (defaults to `FALSE`), see [as.rsi()]
 #' @inheritParams filter_ab_class 
 #' @details \strong{\Sexpr{ifelse(as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2, paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}
 #' 

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/catalogue_of_life.R

@@ -108,15 +108,15 @@ catalogue_of_life_version <- function() {
   
   check_dataset_integrity()
   
-  # see the `catalogue_of_life` list in R/data.R
+  # see the `CATALOGUE_OF_LIFE` list in R/globals.R
   lst <- list(CoL =
-                list(version = gsub("{year}", catalogue_of_life$year, catalogue_of_life$version, fixed = TRUE),
-                     url = gsub("{year}", catalogue_of_life$year, catalogue_of_life$url_CoL, fixed = TRUE),
+                list(version = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE),
+                     url = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$url_CoL, fixed = TRUE),
                      n = nrow(pm_filter(microorganisms, source == "CoL"))),
               LPSN =
                 list(version = "List of Prokaryotic names with Standing in Nomenclature",
-                     url = catalogue_of_life$url_LPSN,
-                     yearmonth = catalogue_of_life$yearmonth_LPSN,
+                     url = CATALOGUE_OF_LIFE$url_LPSN,
+                     yearmonth = CATALOGUE_OF_LIFE$yearmonth_LPSN,
                      n = nrow(pm_filter(microorganisms, source == "LPSN"))),
               total_included =
                 list(

R/data.R

@@ -83,7 +83,7 @@
 
 #' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
 #'
-#' A data set containing the microbial taxonomy, last updated in `r catalogue_of_life$yearmonth_LPSN`, of six kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
+#' A data set containing the microbial taxonomy, last updated in `r CATALOGUE_OF_LIFE$yearmonth_LPSN`, of six kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
 #' @inheritSection catalogue_of_life Catalogue of Life
 #' @format A [data.frame] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
 #' - `mo`\cr ID of microorganism as used by this package
@@ -94,7 +94,7 @@
 #' - `species_id`\cr ID of the species as used by the Catalogue of Life
 #' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
 #' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
-#' - `snomed`\cr SNOMED code of the microorganism. Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
+#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, according to the `r SNOMED_VERSION$current_source` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
 #' @details 
 #' Please note that entries are only based on the Catalogue of Life and the LPSN (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
 #' 
@@ -124,29 +124,25 @@
 #' 
 #' As of February 2020, the regularly augmented LPSN database at DSMZ is the basis of the new LPSN service. The new database was implemented for the Type-Strain Genome Server and augmented in 2018 to store all kinds of nomenclatural information. Data from the previous version of LPSN and from the Prokaryotic Nomenclature Up-to-date (PNU) service were imported into the new system. PNU had been established in 1993 as a service of the Leibniz Institute DSMZ, and was curated by Norbert Weiss, Manfred Kracht and Dorothea Gleim.
 #' @source 
-#' `r gsub("{year}", catalogue_of_life$year, catalogue_of_life$version, fixed = TRUE)`
+#' `r gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE)` as currently implemented in this `AMR` package:
 #' 
 #' * Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org>
 #' 
-#' List of Prokaryotic names with Standing in Nomenclature: `r catalogue_of_life$yearmonth_LPSN`
+#' List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`) as currently implemented in this `AMR` package:
 #' 
 #' * Parte, A.C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Goker, M. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}
 #' * Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
 #' * Parte, A.C. (2014). LPSN — List of Prokaryotic names with Standing in Nomenclature. Nucleic Acids Research, 42, Issue D1, D613–D616; \doi{10.1093/nar/gkt1111}
 #' * Euzeby, J.P. (1997). List of Bacterial Names with Standing in Nomenclature: a Folder Available on the Internet. International Journal of Systematic Bacteriology, 47, 590-592; \doi{10.1099/00207713-47-2-590}
+#' 
+#' `r SNOMED_VERSION$current_source` as currently implemented in this `AMR` package:
+#' 
+#' * Retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
 #' @inheritSection AMR Reference Data Publicly Available
 #' @inheritSection AMR Read more on Our Website!
 #' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
 "microorganisms"
 
-catalogue_of_life <- list(
-  year = 2019,
-  version = "Catalogue of Life: {year} Annual Checklist",
-  url_CoL = "http://www.catalogueoflife.org/col/",
-  url_LPSN = "https://lpsn.dsmz.de",
-  yearmonth_LPSN = "March 2021"
-)
-
 #' Data Set with Previously Accepted Taxonomic Names
 #'
 #' A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by [as.mo()].

R/eucast_rules.R

@@ -23,25 +23,6 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
-# add new version numbers here, and add the rules themselves to "data-raw/eucast_rules.tsv" and rsi_translation
-# (sourcing "data-raw/_internals.R" will process the TSV file)
-EUCAST_VERSION_BREAKPOINTS <- list("11.0" = list(version_txt = "v11.0",
-                                                 year = 2021, 
-                                                 title = "'EUCAST Clinical Breakpoint Tables'",
-                                                 url = "https://www.eucast.org/clinical_breakpoints/"),
-                                   "10.0" = list(version_txt = "v10.0",
-                                                 year = 2020, 
-                                                 title = "'EUCAST Clinical Breakpoint Tables'",
-                                                 url = "https://www.eucast.org/ast_of_bacteria/previous_versions_of_documents/"))
-EUCAST_VERSION_EXPERT_RULES <- list("3.1" = list(version_txt = "v3.1",
-                                                 year = 2016, 
-                                                 title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
-                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"),
-                                    "3.2" = list(version_txt = "v3.2",
-                                                 year = 2020, 
-                                                 title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
-                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"))
-
 format_eucast_version_nr <- function(version, markdown = TRUE) {
   # for documentation - adds title, version number, year and url in markdown language
   lst <- c(EUCAST_VERSION_BREAKPOINTS, EUCAST_VERSION_EXPERT_RULES)
@@ -74,11 +55,11 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
 #' @param verbose a [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
 #' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Can be either `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), reverse = TRUE)`.
 #' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be either `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), reverse = TRUE)`.
-#' @param ampc_cephalosporin_resistance a character value that should be applied for AmpC de-repressed cephalosporin-resistant mutants, defaults to `NA`. Currently only works when `version_expertrules` is `3.2`; '*EUCAST Expert Rules v3.2 on Enterobacterales*' states that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these agents. A value of `NA` for this argument will remove results for these agents, while e.g. a value of `"R"` will make the results for these agents resistant. Use `NULL` to not alter the results for AmpC de-repressed cephalosporin-resistant mutants. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(eucast_rules_file[which(eucast_rules_file$reference.version == 3.2 & eucast_rules_file$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
+#' @param ampc_cephalosporin_resistance a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants, defaults to `NA`. Currently only works when `version_expertrules` is `3.2`; '*EUCAST Expert Rules v3.2 on Enterobacterales*' states that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three agents. A value of `NA` (the default) for this argument will remove results for these three agents, while e.g. a value of `"R"` will make the results for these agents resistant. Use `NULL` or `FALSE` to not alter results for these three agents of AmpC de-repressed cephalosporin-resistant mutants. Using `TRUE` is equal to using `"R"`. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(eucast_rules_file[which(eucast_rules_file$reference.version == 3.2 & eucast_rules_file$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
 #' @param ... column name of an antibiotic, see section *Antibiotics* below
 #' @param ab any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
 #' @param administration route of administration, either `r vector_or(dosage$administration)`
-#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
+#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
 #' @inheritParams first_isolate
 #' @details
 #' **Note:** This function does not translate MIC values to RSI values. Use [as.rsi()] for that. \cr
@@ -101,7 +82,7 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
 #'
 #' The following antibiotics are used for the functions [eucast_rules()] and [mdro()]. These are shown below in the format 'name (`antimicrobial ID`, [ATC code](https://www.whocc.no/atc/structure_and_principles/))', sorted alphabetically:
 #'
-#' `r create_ab_documentation(c("AMC", "AMK", "AMP", "AMX", "ATM", "AVO", "AZL", "AZM", "BAM", "BPR", "CAC", "CAT", "CAZ", "CCP", "CCV", "CCX", "CDC", "CDR", "CDZ", "CEC", "CED", "CEI", "CEM", "CEP", "CFM", "CFM1", "CFP", "CFR", "CFS", "CFZ", "CHE", "CHL", "CID", "CIP", "CLI", "CLR", "CMX", "CMZ", "CND", "COL", "CPD", "CPI", "CPL", "CPM", "CPO", "CPR", "CPT", "CPX", "CRB", "CRD", "CRN", "CRO", "CSL", "CTB", "CTC", "CTF", "CTL", "CTS", "CTT", "CTX", "CTZ", "CXM", "CYC", "CZA", "CZD", "CZO", "CZP", "CZX", "DAL", "DAP", "DIR", "DIT", "DIX", "DIZ", "DKB", "DOR", "DOX", "ENX", "EPC", "ERY", "ETP", "FEP", "FLC", "FLE", "FLR1", "FOS", "FOV", "FOX", "FOX1", "FUS", "GAT", "GEM", "GEN", "GRX", "HAP", "HET", "IPM", "ISE", "JOS", "KAN", "LEX", "LIN", "LNZ", "LOM", "LOR", "LTM", "LVX", "MAN", "MCM", "MEC", "MEM", "MEV", "MEZ", "MFX", "MID", "MNO", "MTM", "NAL", "NEO", "NET", "NIT", "NOR", "NOV", "NVA", "OFX", "OLE", "ORI", "OXA", "PAZ", "PEF", "PEN", "PHN", "PIP", "PLB", "PME", "PRI", "PRL", "PRU", "PVM", "QDA", "RAM", "RFL", "RID", "RIF", "ROK", "RST", "RXT", "SAM", "SBC", "SDI", "SDM", "SIS", "SLF", "SLF1", "SLF10", "SLF11", "SLF12", "SLF13", "SLF2", "SLF3", "SLF4", "SLF5", "SLF6", "SLF7", "SLF8", "SLF9", "SLT1", "SLT2", "SLT3", "SLT4", "SLT5", "SMX", "SPI", "SPX", "STR", "STR1", "SUD", "SUT", "SXT", "SZO", "TAL", "TCC", "TCM", "TCY", "TEC", "TEM", "TGC", "THA", "TIC", "TIO", "TLT", "TLV", "TMP", "TMX", "TOB", "TRL", "TVA", "TZD", "TZP", "VAN"))`
+#' `r create_ab_documentation(c("AMC", "AMK", "AMP", "AMX", "APL", "APX", "ATM", "AVB", "AVO", "AZD", "AZL", "AZM", "BAM", "BPR", "CAC", "CAT", "CAZ", "CCP", "CCV", "CCX", "CDC", "CDR", "CDZ", "CEC", "CED", "CEI", "CEM", "CEP", "CFM", "CFM1", "CFP", "CFR", "CFS", "CFZ", "CHE", "CHL", "CIC", "CID", "CIP", "CLI", "CLM", "CLO", "CLR", "CMX", "CMZ", "CND", "COL", "CPD", "CPI", "CPL", "CPM", "CPO", "CPR", "CPT", "CPX", "CRB", "CRD", "CRN", "CRO", "CSL", "CTB", "CTC", "CTF", "CTL", "CTS", "CTT", "CTX", "CTZ", "CXM", "CYC", "CZA", "CZD", "CZO", "CZP", "CZX", "DAL", "DAP", "DIC", "DIR", "DIT", "DIX", "DIZ", "DKB", "DOR", "DOX", "ENX", "EPC", "ERY", "ETP", "FEP", "FLC", "FLE", "FLR1", "FOS", "FOV", "FOX", "FOX1", "FUS", "GAT", "GEM", "GEN", "GRX", "HAP", "HET", "IPM", "ISE", "JOS", "KAN", "LEN", "LEX", "LIN", "LNZ", "LOM", "LOR", "LTM", "LVX", "MAN", "MCM", "MEC", "MEM", "MET", "MEV", "MEZ", "MFX", "MID", "MNO", "MTM", "NAC", "NAF", "NAL", "NEO", "NET", "NIT", "NOR", "NOV", "NVA", "OFX", "OLE", "ORI", "OXA", "PAZ", "PEF", "PEN", "PHE", "PHN", "PIP", "PLB", "PME", "PNM", "PRC", "PRI", "PRL", "PRP", "PRU", "PVM", "QDA", "RAM", "RFL", "RID", "RIF", "ROK", "RST", "RXT", "SAM", "SBC", "SDI", "SDM", "SIS", "SLF", "SLF1", "SLF10", "SLF11", "SLF12", "SLF13", "SLF2", "SLF3", "SLF4", "SLF5", "SLF6", "SLF7", "SLF8", "SLF9", "SLT1", "SLT2", "SLT3", "SLT4", "SLT5", "SLT6", "SMX", "SPI", "SPX", "SRX", "STR", "STR1", "SUD", "SUL", "SUT", "SXT", "SZO", "TAL", "TAZ", "TCC", "TCM", "TCY", "TEC", "TEM", "TGC", "THA", "TIC", "TIO", "TLT", "TLV", "TMP", "TMX", "TOB", "TRL", "TVA", "TZD", "TZP", "VAN"))`
 #' @aliases EUCAST
 #' @rdname eucast_rules
 #' @export
@@ -176,7 +157,7 @@ eucast_rules <- function(x,
   meet_criteria(verbose, allow_class = "logical", has_length = 1)
   meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))
   meet_criteria(version_expertrules, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_EXPERT_RULES)))
-  meet_criteria(ampc_cephalosporin_resistance, has_length = 1, allow_NA = TRUE, allow_NULL = TRUE, is_in = c("R", "S", "I"))
+  meet_criteria(ampc_cephalosporin_resistance, allow_class = c("logical", "character", "rsi"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE)
   meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
   
   x_deparsed <- deparse(substitute(x))
@@ -287,8 +268,12 @@ eucast_rules <- function(x,
   AMK <- cols_ab["AMK"]
   AMP <- cols_ab["AMP"]
   AMX <- cols_ab["AMX"]
+  APL <- cols_ab["APL"]
+  APX <- cols_ab["APX"]
   ATM <- cols_ab["ATM"]
+  AVB <- cols_ab["AVB"]
   AVO <- cols_ab["AVO"]
+  AZD <- cols_ab["AZD"]
   AZL <- cols_ab["AZL"]
   AZM <- cols_ab["AZM"]
   BAM <- cols_ab["BAM"]
@@ -315,9 +300,12 @@ eucast_rules <- function(x,
   CFZ <- cols_ab["CFZ"]
   CHE <- cols_ab["CHE"]
   CHL <- cols_ab["CHL"]
+  CIC <- cols_ab["CIC"]
   CID <- cols_ab["CID"]
   CIP <- cols_ab["CIP"]
   CLI <- cols_ab["CLI"]
+  CLM <- cols_ab["CLM"]
+  CLO <- cols_ab["CLO"]
   CLR <- cols_ab["CLR"]
   CMX <- cols_ab["CMX"]
   CMZ <- cols_ab["CMZ"]
@@ -353,6 +341,7 @@ eucast_rules <- function(x,
   CZX <- cols_ab["CZX"]
   DAL <- cols_ab["DAL"]
   DAP <- cols_ab["DAP"]
+  DIC <- cols_ab["DIC"]
   DIR <- cols_ab["DIR"]
   DIT <- cols_ab["DIT"]
   DIX <- cols_ab["DIX"]
@@ -383,6 +372,7 @@ eucast_rules <- function(x,
   ISE <- cols_ab["ISE"]
   JOS <- cols_ab["JOS"]
   KAN <- cols_ab["KAN"]
+  LEN <- cols_ab["LEN"]
   LEX <- cols_ab["LEX"]
   LIN <- cols_ab["LIN"]
   LNZ <- cols_ab["LNZ"]
@@ -394,12 +384,15 @@ eucast_rules <- function(x,
   MCM <- cols_ab["MCM"]
   MEC <- cols_ab["MEC"]
   MEM <- cols_ab["MEM"]
+  MET <- cols_ab["MET"]
   MEV <- cols_ab["MEV"]
   MEZ <- cols_ab["MEZ"]
   MFX <- cols_ab["MFX"]
   MID <- cols_ab["MID"]
   MNO <- cols_ab["MNO"]
   MTM <- cols_ab["MTM"]
+  NAC <- cols_ab["NAC"]
+  NAF <- cols_ab["NAF"]
   NAL <- cols_ab["NAL"]
   NEO <- cols_ab["NEO"]
   NET <- cols_ab["NET"]
@@ -414,12 +407,16 @@ eucast_rules <- function(x,
   PAZ <- cols_ab["PAZ"]
   PEF <- cols_ab["PEF"]
   PEN <- cols_ab["PEN"]
+  PHE <- cols_ab["PHE"]
   PHN <- cols_ab["PHN"]
   PIP <- cols_ab["PIP"]
   PLB <- cols_ab["PLB"]
   PME <- cols_ab["PME"]
+  PNM <- cols_ab["PNM"]
+  PRC <- cols_ab["PRC"]
   PRI <- cols_ab["PRI"]
   PRL <- cols_ab["PRL"]
+  PRP <- cols_ab["PRP"]
   PRU <- cols_ab["PRU"]
   PVM <- cols_ab["PVM"]
   QDA <- cols_ab["QDA"]
@@ -454,16 +451,20 @@ eucast_rules <- function(x,
   SLT3 <- cols_ab["SLT3"]
   SLT4 <- cols_ab["SLT4"]
   SLT5 <- cols_ab["SLT5"]
+  SLT6 <- cols_ab["SLT6"]
   SMX <- cols_ab["SMX"]
   SPI <- cols_ab["SPI"]
   SPX <- cols_ab["SPX"]
+  SRX <- cols_ab["SRX"]
   STR <- cols_ab["STR"]
   STR1 <- cols_ab["STR1"]
   SUD <- cols_ab["SUD"]
+  SUL <- cols_ab["SUL"]
   SUT <- cols_ab["SUT"]
   SXT <- cols_ab["SXT"]
   SZO <- cols_ab["SZO"]
   TAL <- cols_ab["TAL"]
+  TAZ <- cols_ab["TAZ"]
   TCC <- cols_ab["TCC"]
   TCM <- cols_ab["TCM"]
   TCY <- cols_ab["TCY"]
@@ -765,10 +766,14 @@ eucast_rules <- function(x,
                                 (reference.rule_group %like% "expert" & reference.version == version_expertrules))
   }
   # filter out AmpC de-repressed cephalosporin-resistant mutants ----
-  if (is.null(ampc_cephalosporin_resistance)) {
+  # cefotaxime, ceftriaxone, ceftazidime
+  if (is.null(ampc_cephalosporin_resistance) || isFALSE(ampc_cephalosporin_resistance)) {
     eucast_rules_df <- subset(eucast_rules_df,
                               !reference.rule %like% "ampc")
   } else {
+    if (isTRUE(ampc_cephalosporin_resistance)) {
+      ampc_cephalosporin_resistance <- "R"
+    }
     eucast_rules_df[which(eucast_rules_df$reference.rule %like% "ampc"), "to_value"] <- as.character(ampc_cephalosporin_resistance)
   }
   

R/filter_ab_class.R

@@ -31,7 +31,7 @@
 #' @param ab_class an antimicrobial class, like `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
 #' @param result an antibiotic result: S, I or R (or a combination of more of them)
 #' @param scope the scope to check which variables to check, can be `"any"` (default) or `"all"`
-#' @param only_rsi_columns a logical to indicate whether only columns must be included that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
+#' @param only_rsi_columns a logical to indicate whether only columns must be included that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
 #' @param ... arguments passed on to [filter_ab_class()]
 #' @details All columns of `x` will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a filter function like e.g. [filter_aminoglycosides()] will include column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
 #' @rdname filter_ab_class

R/first_isolate.R

@@ -37,13 +37,14 @@
 #' @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 whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`)
+#' @param icu_exclude logical to indicate 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 determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see *Details*
+#' @param ignore_I logical to indicate 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 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 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 ... 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*.
@@ -159,6 +160,7 @@ 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())
@@ -188,6 +190,7 @@ 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)
@@ -472,6 +475,14 @@ 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 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()].
@@ -57,7 +57,7 @@
 #' 4. Added total amount of explained variance as a caption in the plot
 #' 5. Cleaned all syntax based on the `lintr` package, fixed grammatical errors and added integrity checks
 #' 6. Updated documentation
-#' @details The colours for labels and points can be changed by adding another scale layer for colour, like `scale_colour_viridis_d()` or `scale_colour_brewer()`.
+#' @details The colours for labels and points can be changed by adding another scale layer for colour, such as `scale_colour_viridis_d()` and `scale_colour_brewer()`.
 #' @rdname ggplot_pca
 #' @export
 #' @examples 

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/globals.R

@@ -23,6 +23,40 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
+# add new version numbers here, and add the rules themselves to "data-raw/eucast_rules.tsv" and rsi_translation
+# (sourcing "data-raw/_internals.R" will process the TSV file)
+EUCAST_VERSION_BREAKPOINTS <- list("11.0" = list(version_txt = "v11.0",
+                                                 year = 2021, 
+                                                 title = "'EUCAST Clinical Breakpoint Tables'",
+                                                 url = "https://www.eucast.org/clinical_breakpoints/"),
+                                   "10.0" = list(version_txt = "v10.0",
+                                                 year = 2020, 
+                                                 title = "'EUCAST Clinical Breakpoint Tables'",
+                                                 url = "https://www.eucast.org/ast_of_bacteria/previous_versions_of_documents/"))
+EUCAST_VERSION_EXPERT_RULES <- list("3.1" = list(version_txt = "v3.1",
+                                                 year = 2016, 
+                                                 title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
+                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"),
+                                    "3.2" = list(version_txt = "v3.2",
+                                                 year = 2020, 
+                                                 title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
+                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"))
+
+SNOMED_VERSION <- list(title = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)",
+                       current_source = "US Edition of SNOMED CT from 1 September 2020",
+                       current_version = 12,
+                       current_oid = "2.16.840.1.114222.4.11.1009",
+                       value_set_name = "Microorganism",
+                       url = "https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009")
+
+CATALOGUE_OF_LIFE <- list(
+  year = 2019,
+  version = "Catalogue of Life: {year} Annual Checklist",
+  url_CoL = "http://www.catalogueoflife.org/col/",
+  url_LPSN = "https://lpsn.dsmz.de",
+  yearmonth_LPSN = "March 2021"
+)
+
 globalVariables(c(".rowid",
                   "ab",
                   "ab_txt",

R/guess_ab_col.R

@@ -30,7 +30,7 @@
 #' @param x a [data.frame]
 #' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x`
 #' @param verbose a logical to indicate whether additional info should be printed
-#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were [transformed to class `<rsi>`]([rsi]) on beforehand (defaults to `FALSE`)
+#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
 #' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.**
 #' @return A column name of `x`, or `NULL` when no result is found.
 #' @export

R/isolate_identifier.R

@@ -1,203 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Data Analysis for R                   #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# LICENCE                                                              #
-# (c) 2018-2021 Berends MS, Luz CF et al.                              #
-# Developed at the University of Groningen, the Netherlands, in        #
-# collaboration with non-profit organisations Certe Medical            #
-# Diagnostics & Advice, and University Medical Center Groningen.       # 
-#                                                                      #
-# This R package is free software; you can freely use and distribute   #
-# it for both personal and commercial purposes under the terms of the  #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
-# the Free Software Foundation.                                        #
-# We created this package for both routine data analysis and academic  #
-# research and it was publicly released in the hope that it will be    #
-# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
-#                                                                      #
-# Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
-# ==================================================================== #
-
-#' Create Identifier of an Isolate
-#' 
-#' This function will paste the microorganism code with all antimicrobial results into one string for each row in a data set. This is useful to compare isolates, e.g. between institutions or regions, when there is no genotyping available.
-#' @inheritSection lifecycle Experimental Lifecycle
-#' @inheritParams eucast_rules
-#' @param cols_ab a character vector of column names of `x`, or (a combination with) an [antibiotic selector function]([ab_class()]), such as [carbapenems()] and [aminoglycosides()]
-#' @rdname isolate_identifier
-#' @export
-#' @inheritSection AMR Read more on Our Website!
-#' @examples 
-#' # automatic selection of microorganism and antibiotics (i.e., all <rsi> columns, see ?as.rsi)
-#' x <- isolate_identifier(example_isolates)
-#' 
-#' # ignore microorganism codes, only use antimicrobial results
-#' x <- isolate_identifier(example_isolates, col_mo = FALSE, cols_ab = c("AMX", "TZP", "GEN", "TOB"))
-#' 
-#' # select antibiotics from certain antibiotic classes
-#' x <- isolate_identifier(example_isolates, cols_ab = c(carbapenems(), aminoglycosides()))
-isolate_identifier <- function(x, col_mo = NULL, cols_ab = NULL) {
-  if (is.null(col_mo)) {
-    col_mo <- search_type_in_df(x, "mo")
-    if (is.null(col_mo)) {
-      # no column found, then ignore the argument
-      col_mo <- FALSE
-    }
-  }
-  
-  if (isFALSE(col_mo)) {
-    # is FALSE then ignore mo column
-    x$col_mo <- ""
-    col_mo <- "col_mo"
-  } else if (!is.null(col_mo)) {
-    x[, col_mo] <- paste0(as.mo(x[, col_mo, drop = TRUE]), "|")
-  }
-  
-  cols_ab <- deparse(substitute(cols_ab)) # support ab class selectors: isolate_identifier(x, cols_ab = carbapenems())
-  if (identical(cols_ab, "NULL")) {
-    cols_ab <- colnames(x)[vapply(FUN.VALUE = logical(1), x, is.rsi)]
-  } else {
-    cols_ab <- tryCatch(colnames(x[, eval(parse(text = cols_ab), envir = parent.frame())]),
-                        # tryCatch adds 4 calls, so total is -5
-                        error = function(e) stop_(e$message, call = -5))
-  }
-  
-  # cope with empty values
-  if (length(cols_ab) == 0 && all(x[, col_mo, drop = TRUE] == "", na.rm = TRUE)) {
-    warning_("in isolate_identifier(): no column with microorganisms and no columns with antimicrobial agents found", call = FALSE)
-  } else if (length(cols_ab) == 0) {
-    warning_("in isolate_identifier(): no columns with antimicrobial agents found", call = FALSE)
-  }
-  
-  out <- x[, c(col_mo, cols_ab), drop = FALSE]
-  out <- do.call(paste, c(out, sep = ""))
-  out <- gsub("NA", ".", out, fixed = TRUE)
-  out <- set_clean_class(out, new_class = c("isolate_identifier", "character"))
-  attr(out, "ab") <- cols_ab
-  out
-}
-
-#' @method all.equal isolate_identifier
-#' @inheritParams base::all.equal
-#' @param ignore_empty_results a logical to indicate whether empty results must be ignored, so that only values R, S and I will be compared
-#' @rdname isolate_identifier
-#' @export
-all.equal.isolate_identifier <- function(target, current, ignore_empty_results = TRUE, ...) {
-  meet_criteria(target, allow_class = "isolate_identifier")
-  meet_criteria(current, allow_class = "isolate_identifier")
-  meet_criteria(ignore_empty_results, allow_class = "logical", has_length = 1)
-  
-  if (isTRUE(all.equal.character(target, current))) {
-    return(TRUE)
-  }
-  # vectorise over both target and current
-  if (length(target) > 1 && length(current) == 1) {
-    current <- rep(current, length(target))
-  } else if (length(current) > 1 && length(target) == 1) {
-    target <- rep(target, length(current))
-  }
-  stop_if(length(target) != length(current),
-          "length of `target` and `current` must be the same, or one must be 1")
-  
-  get_vector <- function(x) {
-    if (grepl("|", x, fixed = TRUE)) {
-      mo <- gsub("(.*)\\|.*", "\\1", x)
-    } else {
-      mo <- NULL
-    }
-    if (grepl("|", x, fixed = TRUE)) {
-      ab <- gsub(".*\\|(.*)", "\\1", x)
-    } else {
-      ab <- x
-    }
-    ab <- strsplit(ab, "")[[1L]]
-    if (is.null(mo)) {
-      out <- as.character(ab)
-      names(out) <- attributes(x)$ab
-    } else {
-      out <- as.character(c(mo, ab))
-      names(out) <- c("mo", attributes(x)$ab)
-    }
-    out
-  }
-  
-  # run it
-  for (i in seq_len(length(target))) {
-    if (i == 1) {
-      df <- data.frame(object = paste0(c("target[", "current["), i, "]"))
-    }
-    trgt <- get_vector(target[i])
-    crnt <- get_vector(current[i])
-    if (ignore_empty_results == TRUE) {
-      diff <- names(trgt[trgt != crnt & trgt != "." & crnt != "."])  
-    } else {
-      diff <- names(trgt[trgt != crnt])
-    }
-    
-  }
-  
-  stop("THIS FUNCTION IS WORK IN PROGRESS AND NOT AVAILABLE IN THIS BETA VERSION")
-  
-}
-
-#' @method print isolate_identifier
-#' @export
-#' @noRd
-print.isolate_identifier <- function(x, ...) {
-  print(as.character(x), ...)
-}
-
-#' @method [ isolate_identifier
-#' @export
-#' @noRd
-"[.isolate_identifier" <- function(x, ...) {
-  y <- NextMethod()
-  attributes(y) <- attributes(x)
-  y
-}
-#' @method [[ isolate_identifier
-#' @export
-#' @noRd
-"[[.isolate_identifier" <- function(x, ...) {
-  y <- NextMethod()
-  attributes(y) <- attributes(x)
-  y
-}
-#' @method [<- isolate_identifier
-#' @export
-#' @noRd
-"[<-.isolate_identifier" <- function(i, j, ..., value) {
-  y <- NextMethod()
-  attributes(y) <- attributes(i)
-  y
-}
-#' @method [[<- isolate_identifier
-#' @export
-#' @noRd
-"[[<-.isolate_identifier" <- function(i, j, ..., value) {
-  y <- NextMethod()
-  attributes(y) <- attributes(i)
-  y
-}
-#' @method c isolate_identifier
-#' @export
-#' @noRd
-c.isolate_identifier <- function(x, ...) {
-  y <- NextMethod()
-  attributes(y) <- attributes(x)
-  y
-}
-
-#' @method unique isolate_identifier
-#' @export
-#' @noRd
-unique.isolate_identifier <- function(x, incomparables = FALSE, ...) {
-  y <- NextMethod()
-  attributes(y) <- attributes(x)
-  y
-}

R/lifecycle.R

@@ -27,10 +27,10 @@
 # NOTE TO SELF: could also have done this with the 'lifecycle' package, but why add a package dependency for such an easy job??
 ###############
 
-#' Lifecycles of Functions in the `amr` Package
+#' Lifecycles of Functions in the `AMR` Package
 #' @name lifecycle
 #' @rdname lifecycle
-#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://www.Tidyverse.org/lifecycle).
+#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://lifecycle.r-lib.org/articles/stages.html).
 #' 
 #' \if{html}{\figure{lifecycle_tidyverse.svg}{options: height=200px style=margin-bottom:5px} \cr}
 #' This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions. 

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()], [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.
 #' @aliases mic
 #' @export
 #' @seealso [as.rsi()]
@@ -62,13 +100,13 @@
 #' plot(mic_data)
 #' plot(mic_data, mo = "E. coli", ab = "cipro")
 as.mic <- function(x, na.rm = FALSE) {
-  meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer"), allow_NA = TRUE)
+  meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
   meet_criteria(na.rm, allow_class = "logical", has_length = 1)
   
   if (is.mic(x)) {
     x
   } else {
-    x <- unlist(x)
+    x <- as.character(unlist(x))
     if (na.rm == TRUE) {
       x <- x[!is.na(x)]
     }
@@ -80,29 +118,29 @@ 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)
+    x <- gsub("(<|=|>) +", "\\1", x, perl = TRUE)
     # 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)
+    x <- gsub("([^0-9]|^)[.]", "\\10.", x, perl = TRUE)
     # values like "<=0.2560.512" should be 0.512
-    x <- gsub(".*[.].*[.]", "0.", x)
+    x <- gsub(".*[.].*[.]", "0.", x, perl = TRUE)
     # remove ending .0
-    x <- gsub("[.]+0$", "", x)
+    x <- gsub("[.]+0$", "", x, perl = TRUE)
     # remove all after last digit
-    x <- gsub("[^0-9]+$", "", x)
+    x <- gsub("[^0-9]+$", "", x, perl = TRUE)
     # keep only one zero before dot
-    x <- gsub("0+[.]", "0.", x)
+    x <- gsub("0+[.]", "0.", x, perl = TRUE)
     # 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+$", "\\1", x)
-    x <- gsub("(.*[.])0+$", "\\10", x)
+    x <- gsub("([.].?)0+$", "\\1", x, perl = TRUE)
+    x <- gsub("(.*[.])0+$", "\\10", x, perl = TRUE)
     # remove ending .0 again
     x[x %like% "[.]"] <- gsub("0+$", "", x[x %like% "[.]"])
     # never end with dot
-    x <- gsub("[.]$", "", x)
+    x <- gsub("[.]$", "", x, perl = TRUE)
     # force to be character
     x <- as.character(x)
     # trim it
@@ -161,21 +199,21 @@ is.mic <- function(x) {
 #' @export
 #' @noRd
 as.double.mic <- function(x, ...) {
-  as.double(gsub("[<=>]+", "", as.character(x)))
+  as.double(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }
 
 #' @method as.integer mic
 #' @export
 #' @noRd
 as.integer.mic <- function(x, ...) {
-  as.integer(gsub("[<=>]+", "", as.character(x)))
+  as.integer(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }
 
 #' @method as.numeric mic
 #' @export
 #' @noRd
 as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub("[<=>]+", "", as.character(x)))
+  as.numeric(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }
 
 #' @method droplevels mic
@@ -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 +250,24 @@ 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 as.matrix mic
+#' @export
+#' @noRd
+as.matrix.mic <- function(x, ...) {
+  as.matrix(as.double(x), ...)
 }
 
 #' @method [ mic
@@ -281,81 +322,50 @@ 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
+#' @importFrom graphics hist
 #' @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 optimal plotting of 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 = ~stats::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
+#' @importFrom stats median
 #' @export
 #' @noRd
 median.mic <- function(x, na.rm = FALSE, ...) {
@@ -363,26 +373,241 @@ 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), props = props, na.rm = na.rm, names = names, type = type, ...)
+  quantile(as.double(x), probs = probs, 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 +658,27 @@ 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 +721,47 @@ 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/mo.R

@@ -106,7 +106,9 @@
 #' 2. Becker K *et al.* **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
 #' 3. Becker K *et al.* **Emergence of coagulase-negative staphylococci** 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
 #' 4. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95; \doi{10.1084/jem.57.4.571}
-#' 5. Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
+#' 5. `r gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE)`, <http://www.catalogueoflife.org>
+#' 6. List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`), \doi{10.1099/ijsem.0.004332}
+#' 7. `r SNOMED_VERSION$current_source`, retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
 #' @export
 #' @return A [character] [vector] with additional class [`mo`]
 #' @seealso [microorganisms] for the [data.frame] that is being used to determine ID's.

R/mo_matching_score.R

@@ -44,7 +44,7 @@
 #' * \ifelse{html}{\out{<i>p<sub>n</sub></i> is the human pathogenic prevalence group of <i>n</i>, as described below;}}{p_n is the human pathogenic prevalence group of \eqn{n}, as described below;}
 #' * \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
 #' 
-#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*,\\*Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*,*Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
+#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*, *Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*,*Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
 #' 
 #' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first. 
 #' @export

R/mo_property.R

@@ -51,6 +51,8 @@
 #' All output [will be translated][translate] where possible.
 #'
 #' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
+#' 
+#' SNOMED codes - [mo_snomed()] - are from the `r SNOMED_VERSION$current_source`. See the [microorganisms] data set for more info.
 #' @inheritSection mo_matching_score Matching Score for Microorganisms
 #' @inheritSection catalogue_of_life Catalogue of Life
 #' @inheritSection as.mo Source
@@ -60,7 +62,7 @@
 #' - An [integer] in case of [mo_year()]
 #' - A [list] in case of [mo_taxonomy()] and [mo_info()]
 #' - A named [character] in case of [mo_url()]
-#' - A [double] in case of [mo_snomed()]
+#' - A [numeric] in case of [mo_snomed()]
 #' - A [character] in all other cases
 #' @export
 #' @seealso [microorganisms]
@@ -161,7 +163,8 @@
 #' 
 #' # get a list with the complete taxonomy (from kingdom to subspecies)
 #' mo_taxonomy("E. coli")
-#' # get a list with the taxonomy, the authors, Gram-stain and URL to the online database
+#' # get a list with the taxonomy, the authors, Gram-stain,
+#' #   SNOMED codes, and URL to the online database
 #' mo_info("E. coli")
 #' }
 mo_name <- function(x, language = get_locale(), ...) {
@@ -629,7 +632,8 @@ mo_info <- function(x, language = get_locale(),  ...) {
       list(synonyms = mo_synonyms(y),
            gramstain = mo_gramstain(y, language = language),
            url = unname(mo_url(y, open = FALSE)),
-           ref = mo_ref(y))))
+           ref = mo_ref(y),
+           snomed = unlist(mo_snomed(y)))))
   if (length(info) > 1) {
     names(info) <- mo_name(x)
     result <- info
@@ -659,10 +663,10 @@ mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
   df <- data.frame(mo, stringsAsFactors = FALSE) %pm>%
     pm_left_join(pm_select(microorganisms, mo, source, species_id), by = "mo")
   df$url <- ifelse(df$source == "CoL",
-                   paste0(catalogue_of_life$url_CoL, "details/species/id/", df$species_id, "/"),
+                   paste0(CATALOGUE_OF_LIFE$url_CoL, "details/species/id/", df$species_id, "/"),
                    NA_character_)
   u <- df$url
-  u[mo_kingdom(mo) == "Bacteria"] <- paste0(catalogue_of_life$url_LPSN, "/species/", gsub(" ", "-", tolower(mo_names), fixed = TRUE))
+  u[mo_kingdom(mo) == "Bacteria"] <- paste0(CATALOGUE_OF_LIFE$url_LPSN, "/species/", gsub(" ", "-", tolower(mo_names), fixed = TRUE))
   u[mo_kingdom(mo) == "Bacteria" & mo_rank(mo) == "genus"] <- gsub("/species/",
                                                                    "/genus/",
                                                                    u[mo_kingdom(mo) == "Bacteria" & mo_rank(mo) == "genus"],

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
@@ -525,10 +524,9 @@ 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)
 }
 
-
 #' @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 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 (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)
   } 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`

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)) {
+  if (!is.null(cur_column_dplyr) && tryCatch(is.data.frame(get_current_data("ab", 0)), error = function(e) FALSE)) {
     # 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)) {
+  if (!is.null(cur_column_dplyr) && tryCatch(is.data.frame(get_current_data("ab", 0)), error = function(e) FALSE)) {
     # try to get current column, which will only be available when in across()
     ab <- tryCatch(cur_column_dplyr(),
                    error = function(e) ab)
@@ -704,7 +704,6 @@ 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)
@@ -721,10 +720,10 @@ exec_as.rsi <- function(method,
   warned <- FALSE
   method_param <- toupper(method)
   
-  genera <- mo_genus(mo)
-  mo_genus <- as.mo(genera)
-  mo_family <- as.mo(mo_family(mo))
-  mo_order <- as.mo(mo_order(mo))
+  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))
   if (any(genera == "Staphylococcus", na.rm = TRUE)) {
     mo_becker <- as.mo(mo, Becker = TRUE)
   } else {
@@ -808,23 +807,19 @@ 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])) {
+      if (is.na(x[i]) | (is.na(get_record$breakpoint_S) & is.na(get_record$breakpoint_R))) {
         new_rsi[i] <- NA_character_
       } else if (method == "mic") {
-        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",
+        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",
                                       # start interpreting: EUCAST uses <= S and > R, CLSI uses <=S and >= 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",
+                                      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$breakpoint_R ~ "R",
                                       # return "I" when not match the bottom or top
                                       !is.na(get_record$breakpoint_S) & !is.na(get_record$breakpoint_R) ~ "I",
                                       # and NA otherwise

README.md

@@ -10,13 +10,13 @@
 
 <img src="https://msberends.github.io/AMR/works_great_on.png" align="center" height="150px" />
 
-The latest built **source package** (`AMR_latest.tar.gz`) can be found in folder [/data-raw/](data-raw).
+The latest built **source package** (`AMR_latest.tar.gz`) can be found in folder [/data-raw/](https://github.com/msberends/AMR/tree/master/data-raw).
 
-`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 150 countries.
  
-After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
 
-This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
+This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
 
 This is the development source of the `AMR` package for R. Not a developer? Then please visit our website [https://msberends.github.io/AMR/](https://msberends.github.io/AMR/) to read more about this package.
 

_pkgdown.yml

@@ -143,7 +143,6 @@ reference:
     - "`as.mic`"
     - "`as.disk`"
     - "`eucast_rules`"
-    - "`isolate_identifier`"
     
   - title: "Analysing data: antimicrobial resistance"
     desc: >

cran-comments.md

@@ -1 +1 @@
-* Ever since one of the first CRAN releases, CHECK returns a NOTE for having a data and R directory over 3 MB. This is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package.
+* This package has a tarball size of over 7 MB and an installation size of over 5 MB, which will return a NOTE on R CMD CHECK. The package size is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package. This was written and explained in a manuscript that was accepted for publication in the Journal of Statistical Software 4 weeks ago. We will add the paper as a vignette in the next version. Please allow this exception in package size for CRAN. We already compressed all data sets using `compression = "xz"` to make them as small as possible.

data-raw/_internals.R

@@ -219,11 +219,11 @@ changed_md5 <- function(object) {
     compared
   }, error = function(e) TRUE)
 }
-usethis::ui_done(paste0("Saving raw data to {usethis::ui_value('/data-raw/')}"))
 
 # give official names to ABs and MOs
 rsi <- dplyr::mutate(rsi_translation, ab = ab_name(ab), mo = mo_name(mo))
 if (changed_md5(rsi)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('rsi_translation')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(rsi)
   try(saveRDS(rsi, "data-raw/rsi_translation.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(rsi, "data-raw/rsi_translation.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
@@ -235,16 +235,18 @@ if (changed_md5(rsi)) {
 
 mo <- dplyr::mutate_if(microorganisms, ~!is.numeric(.), as.character)
 if (changed_md5(mo)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('microorganisms')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(mo)
   try(saveRDS(mo, "data-raw/microorganisms.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(mo, "data-raw/microorganisms.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(mo, "data-raw/microorganisms.sas"), silent = TRUE)
-  try(haven::write_sav(mo, "data-raw/microorganisms.sav"), silent = TRUE)
-  try(haven::write_dta(mo, "data-raw/microorganisms.dta"), silent = TRUE)
+  try(haven::write_sas(dplyr::select(mo, -snomed), "data-raw/microorganisms.sas"), silent = TRUE)
+  try(haven::write_sav(dplyr::select(mo, -snomed), "data-raw/microorganisms.sav"), silent = TRUE)
+  try(haven::write_dta(dplyr::select(mo, -snomed), "data-raw/microorganisms.dta"), silent = TRUE)
   try(openxlsx::write.xlsx(mo, "data-raw/microorganisms.xlsx"), silent = TRUE)
 }
 
 if (changed_md5(microorganisms.old)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('microorganisms.old')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(microorganisms.old)
   try(saveRDS(microorganisms.old, "data-raw/microorganisms.old.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(microorganisms.old, "data-raw/microorganisms.old.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
@@ -256,6 +258,7 @@ if (changed_md5(microorganisms.old)) {
 
 ab <- dplyr::mutate_if(antibiotics, ~!is.numeric(.), as.character)
 if (changed_md5(ab)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('antibiotics')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(ab)
   try(saveRDS(ab, "data-raw/antibiotics.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(ab, "data-raw/antibiotics.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
@@ -267,6 +270,7 @@ if (changed_md5(ab)) {
 
 av <- dplyr::mutate_if(antivirals, ~!is.numeric(.), as.character)
 if (changed_md5(av)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('antivirals')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(av)
   try(saveRDS(av, "data-raw/antivirals.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(av, "data-raw/antivirals.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
@@ -277,6 +281,7 @@ if (changed_md5(av)) {
 }
 
 if (changed_md5(intrinsic_resistant)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('intrinsic_resistant')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(intrinsic_resistant)
   try(saveRDS(intrinsic_resistant, "data-raw/intrinsic_resistant.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(intrinsic_resistant, "data-raw/intrinsic_resistant.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
@@ -287,6 +292,7 @@ if (changed_md5(intrinsic_resistant)) {
 }
 
 if (changed_md5(dosage)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('dosage')} to {usethis::ui_value('/data-raw/')}"))
   write_md5(dosage)
   try(saveRDS(dosage, "data-raw/dosage.rds", version = 2, compress = "xz"), silent = TRUE)
   try(write.table(dosage, "data-raw/dosage.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)

data-raw/ab.md5

@@ -1 +1 @@
-fa68ab044001078f290218a7de6cc5c4
+77f6cca42687a0e3b1b1045a2d70b226

data-raw/antibiotics.txt

@@ -108,7 +108,7 @@
 "CPT"	"J01DI02"	56841980	"Ceftaroline"	"Cephalosporins (5th gen.)"			"c(\"\", \"cfro\")"	"c(\"teflaro\", \"zinforo\")"			1.2		"character(0)"
 "CPA"			"Ceftaroline/avibactam"	"Cephalosporins (5th gen.)"			""	""					""
 "CAZ"	"J01DD02"	5481173	"Ceftazidime"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"caz\", \"cefta\", \"cfta\", \"cftz\", \"taz\", \"tz\", \"xtz\")"	"c(\"ceftazidim\", \"ceftazidima\", \"ceftazidime\", \"ceftazidimum\", \"ceptaz\", \"fortaz\", \"fortum\", \"pentacef\", \"tazicef\", \"tazidime\")"			4	"g"	"c(\"21151-6\", \"3449-6\", \"80960-8\")"
-"CZA"			"Ceftazidime/avibactam"	"Cephalosporins (3rd gen.)"			"c(\"\", \"cfav\")"	""					""
+"CZA"	"J01DD52"	90643431	"Ceftazidime/avibactam"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"\", \"cfav\")"	"c(\"avycaz\", \"zavicefta\")"			6	"g"	""
 "CCV"	"J01DD52"	9575352	"Ceftazidime/clavulanic acid"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"czcl\", \"xtzl\")"	""			6		""
 "CEM"		6537431	"Cefteram"	"Cephalosporins (3rd gen.)"			""	"c(\"cefteram\", \"cefterame\", \"cefteramum\", \"ceftetrame\")"					"character(0)"
 "CPL"		5362114	"Cefteram pivoxil"	"Cephalosporins (3rd gen.)"			""	"c(\"cefteram pivoxil\", \"tomiron\")"					"character(0)"

data-raw/mo.md5

@@ -1 +1 @@
-8c6d0e8e487d19d9a429abd64fce9290
+82bd6236cf159569f6f5c99f48f92d86

data-raw/reproduction_of_antibiotics.R

@@ -166,7 +166,7 @@ abx2$abbr <- lapply(as.list(abx2$abbr), function(x) unlist(strsplit(x, "|", fixe
 # vector with official names, returns vector with CIDs
 get_CID <- function(ab) {
   CID <- rep(NA_integer_, length(ab))
-  p <- progress_estimated(n = length(ab), min_time = 0)
+  p <- progress_ticker(n = length(ab), min_time = 0)
   for (i in 1:length(ab)) {
     p$tick()$print()
 
@@ -208,10 +208,10 @@ abx2[is.na(CIDs),] %>% View()
 # returns list with synonyms (brand names), with CIDs as names
 get_synonyms <- function(CID, clean = TRUE) {
   synonyms <- rep(NA_character_, length(CID))
-  p <- progress_estimated(n = length(CID), min_time = 0)
+  #p <- progress_ticker(n = length(CID), min_time = 0)
 
   for (i in 1:length(CID)) {
-    p$tick()$print()
+    #p$tick()$print()
 
     synonyms_txt <- ""
 
@@ -564,6 +564,14 @@ antibiotics[which(antibiotics$ab == "CPT"), "atc"] <- "J01DI02"
 antibiotics[which(antibiotics$ab == "FAR"), "atc"] <- "J01DI03"
 # ceftobiprole
 antibiotics[which(antibiotics$ab == "BPR"), "atc"] <- "J01DI01"
+# ceftazidime / avibactam
+antibiotics[which(antibiotics$ab == "CZA"), "atc"] <- "J01DD52"
+antibiotics[which(antibiotics$ab == "CZA"), "cid"] <- 90643431
+antibiotics[which(antibiotics$ab == "CZA"), "atc_group1"] <- "Other beta-lactam antibacterials"
+antibiotics[which(antibiotics$ab == "CZA"), "atc_group2"] <- "Third-generation cephalosporins"
+antibiotics[which(antibiotics$ab == "CZA"), "iv_ddd"] <- 6
+antibiotics[which(antibiotics$ab == "CZA"), "iv_units"] <- "g"
+antibiotics[which(antibiotics$ab == "CZA"), "synonyms"] <- list(c("Avycaz", "Zavicefta"))
 
 # typo
 antibiotics[which(antibiotics$ab == "RXT"), "name"] <- "Roxithromycin"

data-raw/snomed.R

@@ -26,89 +26,44 @@
 library(AMR)
 library(tidyverse)
 
-# go to https://www.nictiz.nl/standaardisatie/terminologiecentrum/referentielijsten/micro-organismen/ (Ctrl/Cmd + A in table)
-# read the table from clipboard
-snomed <- clipr::read_clip_tbl(skip = 2)
+# we will use Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)
+# as a source, which copies directly from the latest US SNOMED CT version
+# - go to https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009
+# - check that current online version is higher than SNOMED_VERSION$current_version
+# - if so, click on 'Download Value Set', choose 'TXT'
+snomed <- read_tsv("data-raw/SNOMED_PHVS_Microorganism_CDC_V12.txt", skip = 3) %>% 
+  select(1:2) %>% 
+  set_names(c("snomed", "mo"))
+
+# save all valid genera, species and subspecies
+vctr <- unique(unlist(strsplit(c(microorganisms$fullname, microorganisms.old$fullname), " ")))
+vctr <- tolower(vctr[vctr %like% "^[a-z]+$"])
+
+# remove all parts of the name that are no valid values in genera, species or subspecies
 snomed <- snomed %>% 
-  dplyr::filter(gsub("(^genus |^familie |^stam |ss.? |subsp.? |subspecies )", "", 
-                     Omschrijving.,
-                     ignore.case = TRUE) %in% c(microorganisms$fullname, 
-                                                microorganisms.old$fullname)) %>% 
-  dplyr::transmute(fullname = mo_name(Omschrijving.),
-                   snomed = as.integer(Id)) %>% 
-  dplyr::filter(!fullname %like% "unknown")
-snomed_trans <- snomed %>%
-  group_by(fullname) %>%
-  mutate(snomed_list = list(snomed)) %>%
-  ungroup() %>%
-  select(fullname, snomed = snomed_list) %>%
-  distinct(fullname, .keep_all = TRUE)
+  mutate(fullname = vapply(FUN.VALUE = character(1),
+                           # split on space and/or comma
+                           strsplit(tolower(mo), "[ ,]"),
+                           function(x) trimws(paste0(x[x %in% vctr], collapse = " "))),
+         # remove " group"
+         fullname = gsub(" group", "", fullname, fixed = TRUE))
 
-microorganisms <- AMR::microorganisms %>% 
-  left_join(snomed_trans)
-# remove the NULLs, set to NA
-microorganisms$snomed <- lapply(microorganisms$snomed, function(x) if (length(x) == 0)  NA else x)
+snomed_keep <- snomed %>% 
+  filter(fullname %in% tolower(c(microorganisms$fullname, microorganisms.old$fullname))) %>% 
+  group_by(fullname_lower = fullname) %>% 
+  summarise(snomed = list(snomed))
 
-microorganisms <- dataset_UTF8_to_ASCII(microorganisms)
+# save to microorganisms data set
+microorganisms <- microorganisms %>%
+  # remove old snomed
+  select(-snomed) %>%
+  # create dummy var for joining
+  mutate(fullname_lower = tolower(fullname)) %>%
+  # join new snomed
+  left_join(snomed_keep) %>%
+  # remove dummy var
+  select(-fullname_lower) %>% 
+  AMR:::dataset_UTF8_to_ASCII()
+usethis::use_data(microorganisms, overwrite = TRUE, compress = "xz")
 
-usethis::use_data(microorganisms, overwrite = TRUE)
-rm(microorganisms)
-
-# OLD ---------------------------------------------------------------------
-
-# baseUrl <- 'https://browser.ihtsdotools.org/snowstorm/snomed-ct'
-# edition <- 'MAIN'
-# version <- '2019-07-31'
-# 
-# microorganisms.snomed <- data.frame(conceptid = character(0),
-#                                     mo = character(0),
-#                                     stringsAsFactors = FALSE)
-# microorganisms$snomed <- ""
-# 
-# # for (i in 1:50) {
-# for (i in 1:1000) {
-#   
-#   if (i %% 10 == 0) {
-#     cat(paste0(i, " - ", cleaner::percentage(i / nrow(microorganisms)), "\n"))
-#   }
-#   
-#   mo_data <- microorganisms %>% 
-#     filter(mo == microorganisms$mo[i]) %>% 
-#     as.list()
-#   
-#   if (!mo_data$rank %in% c("genus", "species")) {
-#     next
-#   }
-#   
-#   searchTerm <- paste0(
-#     ifelse(mo_data$rank == "genus", "Genus ", ""),
-#     mo_data$fullname, 
-#     " (organism)")
-#   
-#   url <- paste0(baseUrl, '/browser/',
-#                 edition, '/', 
-#                 version, 
-#                 '/descriptions?term=', curl::curl_escape(searchTerm),
-#                 '&mode=fullText&activeFilter=true&limit=', 250)
-#   results <- url %>% 
-#     httr::GET() %>%
-#     httr::content(type = "text", encoding = "UTF-8") %>% 
-#     jsonlite::fromJSON(flatten = TRUE) %>% 
-#     .$items
-#   if (NROW(results) == 0) {
-#     next
-#   } else {
-#     message("Adding ", crayon::italic(mo_data$fullname))
-#   }
-#   
-#   tryCatch(
-#     microorganisms$snomed[i] <- results %>% filter(term == searchTerm) %>% pull(concept.conceptId),
-#     error = function(e) invisible()
-#   )
-#   
-#   if (nrow(results) > 1) {
-#       microorganisms.snomed <- microorganisms.snomed %>% 
-#         bind_rows(tibble(conceptid = results %>% filter(term != searchTerm) %>% pull(concept.conceptId) %>% unique(),
-#                          mo = as.character(mo_data$mo)))
-#   }
-# }
+# don't forget to update the version number in SNOMED_VERSION in ./R/globals.R!

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	Gevoelig, 'incr. exposure'	Susceptible, 'incr. exposure'			
+Incr. exposure	TRUE	FALSE	FALSE	Empfindlich, erh Belastung	'Incr. exposure'	'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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 

docs/articles/datasets.html

@@ -39,7 +39,7 @@
       </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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -187,12 +187,12 @@
 
       
 
-      </header><script src="datasets_files/header-attrs-2.6/header-attrs.js"></script><div class="row">
+      </header><script src="datasets_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
   <div class="col-md-9 contents">
     <div class="page-header toc-ignore">
       <h1 data-toc-skip>Data sets for download / own use</h1>
             
-            <h4 class="date">05 March 2021</h4>
+            <h4 class="date">11 March 2021</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/datasets.Rmd"><code>vignettes/datasets.Rmd</code></a></small>
       <div class="hidden name"><code>datasets.Rmd</code></div>
@@ -209,23 +209,24 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="microorganisms-currently-accepted-names" class="section level2">
 <h2 class="hasAnchor">
 <a href="#microorganisms-currently-accepted-names" class="anchor"></a>Microorganisms (currently accepted names)</h2>
-<p>A data set with 70,026 rows and 16 columns, containing the following column names:<br><em>class</em>, <em>family</em>, <em>fullname</em>, <em>genus</em>, <em>kingdom</em>, <em>mo</em>, <em>order</em>, <em>phylum</em>, <em>prevalence</em>, <em>rank</em>, <em>ref</em>, <em>snomed</em>, <em>source</em>, <em>species</em>, <em>species_id</em> and <em>subspecies</em>.</p>
+<p>A data set with 70,026 rows and 16 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>, <em>species_id</em>, <em>source</em>, <em>prevalence</em> and <em>snomed</em>.</p>
 <p>This data set is in R available as <code>microorganisms</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
+<p>It was last updated on 11 March 2021 20:59:32 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.rds">R file</a> (2.2 MB)<br>
 </li>
-<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.xlsx">Excel file</a> (6.3 MB)<br>
+<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.xlsx">Excel file</a> (6.4 MB)<br>
 </li>
-<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.txt">plain text file</a> (13.9 MB)<br>
+<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.txt">plain text file</a> (14.8 MB)<br>
 </li>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sas">SAS file</a> (27.4 MB)<br>
 </li>
-<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav">SPSS file</a> (29.9 MB)<br>
+<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.sav">SPSS file</a> (27.8 MB)<br>
 </li>
-<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta">Stata file</a> (26.9 MB)</li>
+<li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.dta">Stata file</a> (25.1 MB)</li>
 </ul>
+<p><strong>NOTE: The exported files for SAS, SPSS and Stata do not contain SNOMED codes, as their file size would exceed 100 MB; the file size limit of GitHub.</strong> Advice? Use R instead.</p>
 <div id="source" class="section level3">
 <h3 class="hasAnchor">
 <a href="#source" class="anchor"></a>Source</h3>
@@ -235,6 +236,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <a href="http://www.catalogueoflife.org">Catalogue of Life</a> (included version: 2019)</li>
 <li>
 <a href="https://lpsn.dsmz.de">List of Prokaryotic names with Standing in Nomenclature</a> (LPSN, last updated: March 2021)</li>
+<li>US Edition of SNOMED CT from 1 September 2020, retrieved from the <a href="https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009">Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)</a>, OID 2.16.840.1.114222.4.11.1009, version 12</li>
 </ul>
 </div>
 <div id="example-content" class="section level3">
@@ -276,22 +278,22 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <p>Example rows when filtering on genus <em>Escherichia</em>:</p>
 <table class="table">
 <colgroup>
-<col width="5%">
-<col width="9%">
-<col width="3%">
-<col width="6%">
+<col width="4%">
 <col width="8%">
-<col width="6%">
-<col width="7%">
+<col width="3%">
 <col width="5%">
+<col width="7%">
+<col width="6%">
+<col width="6%">
+<col width="4%">
 <col width="4%">
 <col width="4%">
 <col width="3%">
-<col width="9%">
-<col width="13%">
-<col width="3%">
-<col width="4%">
+<col width="8%">
+<col width="11%">
+<col width="2%">
 <col width="4%">
+<col width="15%">
 </colgroup>
 <thead><tr class="header">
 <th align="center">mo</th>
@@ -364,7 +366,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <td align="center">3254b3db31bf16fdde669ac57bf8c4fe</td>
 <td align="center">CoL</td>
 <td align="center">1</td>
-<td align="center">112283007</td>
+<td align="center">1095001000112106, 715307006, 737528008, …</td>
 </tr>
 <tr class="even">
 <td align="center">B_ESCHR_FRGS</td>
@@ -418,7 +420,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <td align="center">792928</td>
 <td align="center">LPSN</td>
 <td align="center">1</td>
-<td align="center"></td>
+<td align="center">14961000146107</td>
 </tr>
 </tbody>
 </table>
@@ -427,10 +429,10 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="microorganisms-previously-accepted-names" class="section level2">
 <h2 class="hasAnchor">
 <a href="#microorganisms-previously-accepted-names" class="anchor"></a>Microorganisms (previously accepted names)</h2>
-<p>A data set with 14,100 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>prevalence</em> and <em>ref</em>.</p>
+<p>A data set with 14,100 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and <em>prevalence</em>.</p>
 <p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column <em>‘fullname’</em>. For the scientific reference of the new names, i.e. of column <em>‘fullname_new’</em>, see the <code>microorganisms</code> data set.</p>
 <p>This data set is in R available as <code>microorganisms.old</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
+<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/microorganisms.old.rds">R file</a> (0.2 MB)<br>
@@ -493,9 +495,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="antibiotic-agents" class="section level2">
 <h2 class="hasAnchor">
 <a href="#antibiotic-agents" class="anchor"></a>Antibiotic agents</h2>
-<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>abbreviations</em>, <em>atc</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>cid</em>, <em>group</em>, <em>iv_ddd</em>, <em>iv_units</em>, <em>loinc</em>, <em>name</em>, <em>oral_ddd</em>, <em>oral_units</em> and <em>synonyms</em>.</p>
+<p>A data set with 456 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>atc</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
 <p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 14 January 2021 16:04:41 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
+<p>It was last updated on 11 March 2021 20:59:32 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antibiotics.rds">R file</a> (32 kB)<br>
@@ -661,9 +663,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="antiviral-agents" class="section level2">
 <h2 class="hasAnchor">
 <a href="#antiviral-agents" class="anchor"></a>Antiviral agents</h2>
-<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>atc_group</em>, <em>cid</em>, <em>iv_ddd</em>, <em>iv_units</em>, <em>name</em>, <em>oral_ddd</em>, <em>oral_units</em> and <em>synonyms</em>.</p>
+<p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em> and <em>iv_units</em>.</p>
 <p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 29 August 2020 21:53:07 CEST. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
+<p>It was last updated on 29 August 2020 19:53:07 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/antivirals.rds">R file</a> (5 kB)<br>
@@ -788,9 +790,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="intrinsic-bacterial-resistance" class="section level2">
 <h2 class="hasAnchor">
 <a href="#intrinsic-bacterial-resistance" class="anchor"></a>Intrinsic bacterial resistance</h2>
-<p>A data set with 93,892 rows and 2 columns, containing the following column names:<br><em>antibiotic</em> and <em>microorganism</em>.</p>
+<p>A data set with 93,892 rows and 2 columns, containing the following column names:<br><em>microorganism</em> and <em>antibiotic</em>.</p>
 <p>This data set is in R available as <code>intrinsic_resistant</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
+<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/intrinsic_resistant.rds">R file</a> (69 kB)<br>
@@ -1003,9 +1005,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="interpretation-from-mic-values-disk-diameters-to-rsi" class="section level2">
 <h2 class="hasAnchor">
 <a href="#interpretation-from-mic-values-disk-diameters-to-rsi" class="anchor"></a>Interpretation from MIC values / disk diameters to R/SI</h2>
-<p>A data set with 20,486 rows and 10 columns, containing the following column names:<br><em>ab</em>, <em>breakpoint_R</em>, <em>breakpoint_S</em>, <em>disk_dose</em>, <em>guideline</em>, <em>method</em>, <em>mo</em>, <em>ref_tbl</em>, <em>site</em> and <em>uti</em>.</p>
+<p>A data set with 20,486 rows and 10 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
 <p>This data set is in R available as <code>rsi_translation</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 5 March 2021 10:46:55 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
+<p>It was last updated on 5 March 2021 09:46:55 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/rsi_translation.rds">R file</a> (34 kB)<br>
@@ -1133,9 +1135,9 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <div id="dosage-guidelines-from-eucast" class="section level2">
 <h2 class="hasAnchor">
 <a href="#dosage-guidelines-from-eucast" class="anchor"></a>Dosage guidelines from EUCAST</h2>
-<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>administration</em>, <em>dose</em>, <em>dose_times</em>, <em>eucast_version</em>, <em>name</em>, <em>notes</em>, <em>original_txt</em> and <em>type</em>.</p>
+<p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>, <em>dose_times</em>, <em>administration</em>, <em>notes</em>, <em>original_txt</em> and <em>eucast_version</em>.</p>
 <p>This data set is in R available as <code>dosage</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 25 January 2021 21:58:20 CET. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
+<p>It was last updated on 25 January 2021 20:58:20 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/master/data-raw/../data-raw/dosage.rds">R file</a> (3 kB)<br>

docs/articles/datasets_files/header-attrs-2.7/header-attrs.js

@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});

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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 

docs/extra.js

@@ -66,7 +66,7 @@ $(document).ready(function() {
   // edit footer
   $('footer').html(
     '<div>' +
-      '<p><code>AMR</code> (for R). Developed at the <a href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations <a href="https://www.certe.nl">Certe Medical Diagnostics and Advice</a> and <a href="https://www.umcg.nl">University Medical Center Groningen</a>.</p>' +
+      '<p><code>AMR</code> (for R). Developed at the <a href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations <a href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl">University Medical Center Groningen</a>.</p>' +
             '<a href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/master/docs/logo_rug.png" class="footer_logo"></a>' + 
     '</div>');
   // all links should open in new tab/window

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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -207,11 +207,11 @@ Since you are one of our users, we would like to know how you use the package an
 <a href="#what-is-amr-for-r" class="anchor"></a>What is <code>AMR</code> (for R)?</h3>
 <p><em>(To find out how to conduct AMR data analysis, please <a href="./articles/AMR.html">continue reading here to get started</a>.)</em></p>
 <p><code>AMR</code> is a free, open-source and independent <a href="https://www.r-project.org">R package</a> to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. <strong>Our aim is to provide a standard</strong> for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.</p>
-<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~70,000 distinct microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~550 antibiotic, antimycotic and antiviral drugs</strong></a> by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
-<p>This package is <a href="https://en.wikipedia.org/wiki/Dependency_hell">fully independent of any other R package</a> and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl">University of Groningen</a>, in collaboration with non-profit organisations <a href="https://www.certe.nl">Certe Medical Diagnostics and Advice</a> and <a href="https://www.umcg.nl">University Medical Center Groningen</a>. This R package is <a href="./news">actively maintained</a> and is free software (see <a href="#copyright">Copyright</a>).</p>
+<p>After installing this package, R knows <a href="./reference/microorganisms.html"><strong>~70,000 distinct microbial species</strong></a> and all <a href="./reference/antibiotics.html"><strong>~550 antibiotic, antimycotic and antiviral drugs</strong></a> by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.</p>
+<p>This package is <a href="https://en.wikipedia.org/wiki/Dependency_hell">fully independent of any other R package</a> and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). <strong>It was designed to work in any setting, including those with very limited resources</strong>. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl">University of Groningen</a>, in collaboration with non-profit organisations <a href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a href="https://www.umcg.nl">University Medical Center Groningen</a>. This R package is <a href="./news">actively maintained</a> and is free software (see <a href="#copyright">Copyright</a>).</p>
 <div class="main-content" style="display: inline-block;">
 <p>
-<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a> <strong>Used in 148 countries</strong><br> Since its first public release in early 2018, this package has been downloaded from 148 countries. Click the map to enlarge and to see the country names.
+<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a> <strong>Used in 155 countries</strong><br> Since its first public release in early 2018, this package has been downloaded from 155 countries. Click the map to enlarge and to see the country names.
 </p>
 </div>
 <div id="with-amr-for-r-theres-always-a-knowledgeable-microbiologist-by-your-side" class="section level5">
@@ -349,7 +349,7 @@ Since you are one of our users, we would like to know how you use the package an
 <a href="#partners" class="anchor"></a>Partners</h4>
 <p>The development of this package is part of, related to, or made possible by:</p>
 <div align="center">
-<p><a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a> <a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a> <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice"><img src="./logo_certe.png" class="partner_logo"></a> <a href="http://www.eurhealth-1health.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" class="partner_logo"></a> <a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" class="partner_logo"></a></p>
+<p><a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a> <a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a> <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" class="partner_logo"></a> <a href="http://www.eurhealth-1health.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" class="partner_logo"></a> <a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" class="partner_logo"></a></p>
 </div>
 </div>
 </div>
@@ -372,7 +372,7 @@ Since you are one of our users, we would like to know how you use the package an
 <li>Applying EUCAST expert rules (<a href="./reference/eucast_rules.html">manual</a>)</li>
 <li>Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code (<a href="./reference/mo_property.html">manual</a>)</li>
 <li>Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code (<a href="./reference/ab_property.html">manual</a>)</li>
-<li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI (<a href="./articles/datasets.html">link</a>)</li>
+<li>Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to R/SI (<a href="./articles/datasets.html">link</a>)</li>
 <li>Principal component analysis for AMR (<a href="./articles/PCA.html">tutorial</a>)</li>
 </ul>
 </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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -236,14 +236,10 @@
       <small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
     </div>
 
-    <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>
+    <div id="amr-160" class="section level1">
+<h1 class="page-header" data-toc-text="1.6.0">
+<a href="#amr-160" class="anchor"></a>AMR 1.6.0<small> Unreleased </small>
 </h1>
-<div id="last-updated-5-march-2021" class="section level2">
-<h2 class="hasAnchor">
-<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">
 <a href="#new" class="anchor"></a>New</h3>
@@ -279,7 +275,6 @@
 <span class="co">#&gt; Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either "R", "S" or "I"</span></code></pre></div>
 </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><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>
@@ -303,6 +298,22 @@
 <span class="co">#&gt; [1] "Hongos"    "Levaduras"</span></code></pre></div>
 </li>
 <li><p>Added Pretomanid (PMD, J04AK08) to the <code>antibiotics</code> data set</p></li>
+<li>
+<p>MIC values (see <code><a href="../reference/as.mic.html">as.mic()</a></code>) can now be used in any mathematical processing, such as usage inside functions <code><a href="https://rdrr.io/r/base/Extremes.html">min()</a></code>, <code><a href="https://rdrr.io/r/base/Extremes.html">max()</a></code>, <code><a href="https://rdrr.io/r/base/range.html">range()</a></code>, and with binary operators (<code><a href="https://rdrr.io/r/base/Arithmetic.html">+</a></code>, <code><a href="https://rdrr.io/r/base/Arithmetic.html">-</a></code>, etc.). This allows for easy distribution analysis and fast filtering on MIC values:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R">
+<span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random.html">random_mic</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span>
+<span class="va">x</span>
+<span class="co">#&gt; Class &lt;mic&gt;</span>
+<span class="co">#&gt;  [1] 128   0.5   2     0.125 64    0.25  &gt;=256 8     16    4</span>
+<span class="va">x</span><span class="op">[</span><span class="va">x</span> <span class="op">&gt;</span> <span class="fl">4</span><span class="op">]</span>
+<span class="co">#&gt; Class &lt;mic&gt;</span>
+<span class="co">#&gt; [1] 128   64    &gt;=256 8     16</span>
+<span class="fu"><a href="https://rdrr.io/r/base/range.html">range</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
+<span class="co">#&gt; [1]   0.125 256.000</span>
+<span class="fu"><a href="https://rdrr.io/r/base/range.html">range</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html">log2</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span>
+<span class="co">#&gt; [1] -3  8</span></code></pre></div>
+</li>
 </ul>
 </div>
 <div id="changed" class="section level3">
@@ -324,12 +335,12 @@
 <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>Updated SNOMED codes to US Edition of SNOMED CT from 1 September 2020 and added the source to the help page of the <code>microorganisms</code> data set</li>
 <li>
 <code><a href="../reference/as.rsi.html">is.rsi()</a></code> and <code><a href="../reference/as.rsi.html">is.rsi.eligible()</a></code> now return a vector of <code>TRUE</code>/<code>FALSE</code> when the input is a data set, by iterating over all columns</li>
 <li>Using functions without setting a data set (e.g., <code><a href="../reference/mo_property.html">mo_is_gram_negative()</a></code>, <code><a href="../reference/mo_property.html">mo_is_gram_positive()</a></code>, <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code>, <code><a href="../reference/first_isolate.html">first_isolate()</a></code>, <code><a href="../reference/mdro.html">mdro()</a></code>) now work with <code>dplyr</code>s <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> again</li>
 <li>
 <code><a href="../reference/first_isolate.html">first_isolate()</a></code> can be used with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> (also when using a dot <code>.</code> as input for the data) and now returns the names of the groups</li>
-<li>MIC values now allow for any mathematical processing, such as usage inside functions <code><a href="https://rdrr.io/r/base/Extremes.html">min()</a></code>, <code><a href="https://rdrr.io/r/base/Extremes.html">max()</a></code>, <code><a href="https://rdrr.io/r/base/range.html">range()</a></code>, and with binary operators (+, -, etc.). This also enables other functions, such as <code><a href="https://rdrr.io/r/stats/fivenum.html">fivenum()</a></code>.</li>
 <li>Updated the data set <code>microorganisms.codes</code> (which contains popular LIS and WHONET codes for microorganisms) for some species of <em>Mycobacterium</em> that previously incorrectly returned <em>M. africanum</em>
 </li>
 <li>WHONET code <code>"PNV"</code> will now correctly be interpreted as <code>PHN</code>, the antibiotic code for phenoxymethylpenicillin (‘peni V’)</li>
@@ -351,6 +362,7 @@
 </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">
@@ -361,7 +373,6 @@
 <li>Loading the package (i.e., <code><a href="https://msberends.github.io/AMR/">library(AMR)</a></code>) now is ~50 times faster than before, in costs of package size (which increased by ~3 MB)</li>
 </ul>
 </div>
-</div>
 </div>
     <div id="amr-150" class="section level1">
 <h1 class="page-header" data-toc-text="1.5.0">
@@ -373,7 +384,7 @@
 <ul>
 <li>
 <p>Functions <code><a href="../reference/get_episode.html">get_episode()</a></code> and <code><a href="../reference/get_episode.html">is_new_episode()</a></code> to determine (patient) episodes which are not necessarily based on microorganisms. The <code><a href="../reference/get_episode.html">get_episode()</a></code> function returns the index number of the episode per group, while the <code><a href="../reference/get_episode.html">is_new_episode()</a></code> function returns values <code>TRUE</code>/<code>FALSE</code> to indicate whether an item in a vector is the start of a new episode. They also support <code>dplyr</code>s grouping (i.e. using <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code>):</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
 <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
@@ -427,7 +438,7 @@
 <code><a href="../reference/mdro.html">mdr_cmi2012()</a></code>,</li>
 <li><code><a href="../reference/mdro.html">eucast_exceptional_phenotypes()</a></code></li>
 </ul>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># to select first isolates that are Gram-negative </span>
 <span class="co"># and view results of cephalosporins and aminoglycosides:</span>
@@ -439,7 +450,7 @@
 </li>
 <li>
 <p>For antibiotic selection functions (such as <code><a href="../reference/antibiotic_class_selectors.html">cephalosporins()</a></code>, <code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code>) to select columns based on a certain antibiotic group, the dependency on the <code>tidyselect</code> package was removed, meaning that they can now also be used without the need to have this package installed and now also work in base R function calls (they rely on R 3.2 or later):</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># above example in base R:</span>
 <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html">which</a></span><span class="op">(</span><span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span><span class="op">)</span> <span class="op">&amp;</span> <span class="fu"><a href="../reference/mo_property.html">mo_is_gram_negative</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,
@@ -490,7 +501,7 @@
 <li>
 <p>Data set <code>intrinsic_resistant</code>. This data set contains all bug-drug combinations where the ‘bug’ is intrinsic resistant to the ‘drug’ according to the latest EUCAST insights. It contains just two columns: <code>microorganism</code> and <code>antibiotic</code>.</p>
 <p>Curious about which enterococci are actually intrinsic resistant to vancomycin?</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
@@ -513,7 +524,7 @@
 <ul>
 <li>
 <p>Support for using <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/across.html">across()</a></code> to interpret MIC values or disk zone diameters, which also automatically determines the column with microorganism names or codes.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># until dplyr 1.0.0</span>
 <span class="va">your_data</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_if</a></span><span class="op">(</span><span class="va">is.mic</span>, <span class="va">as.rsi</span><span class="op">)</span>
@@ -531,7 +542,7 @@
 </li>
 <li>
 <p>Added intelligent data cleaning to <code><a href="../reference/as.disk.html">as.disk()</a></code>, so numbers can also be extracted from text and decimal numbers will always be rounded up:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/as.disk.html">as.disk</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"disk zone: 23.4 mm"</span>, <span class="fl">23.4</span><span class="op">)</span><span class="op">)</span>
 <span class="co">#&gt; Class &lt;disk&gt;</span>
@@ -592,7 +603,7 @@
 <li><p>Function <code><a href="../reference/ab_from_text.html">ab_from_text()</a></code> to retrieve antimicrobial drug names, doses and forms of administration from clinical texts in e.g. health care records, which also corrects for misspelling since it uses <code><a href="../reference/as.ab.html">as.ab()</a></code> internally</p></li>
 <li>
 <p><a href="https://tidyselect.r-lib.org/reference/language.html">Tidyverse selection helpers</a> for antibiotic classes, that help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. They can be used in any function that allows selection helpers, like <code><a href="https://dplyr.tidyverse.org/reference/select.html">dplyr::select()</a></code> and <code><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">tidyr::pivot_longer()</a></code>:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
 
@@ -781,7 +792,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Fixed important floating point error for some MIC comparisons in EUCAST 2020 guideline</p></li>
 <li>
 <p>Interpretation from MIC values (and disk zones) to R/SI can now be used with <code><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at()</a></code> of the <code>dplyr</code> package:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">yourdata</span> <span class="op">%&gt;%</span> 
   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span><span class="op">(</span><span class="va">antibiotic1</span><span class="op">:</span><span class="va">antibiotic25</span><span class="op">)</span>, <span class="va">as.rsi</span>, mo <span class="op">=</span> <span class="st">"E. coli"</span><span class="op">)</span>
@@ -810,7 +821,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Support for LOINC codes in the <code>antibiotics</code> data set. Use <code><a href="../reference/ab_property.html">ab_loinc()</a></code> to retrieve LOINC codes, or use a LOINC code for input in any <code>ab_*</code> function:</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/ab_property.html">ab_loinc</a></span><span class="op">(</span><span class="st">"ampicillin"</span><span class="op">)</span>
 <span class="co">#&gt; [1] "21066-6" "3355-5"  "33562-0" "33919-2" "43883-8" "43884-6" "87604-5"</span>
@@ -821,7 +832,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>Support for SNOMED CT codes in the <code>microorganisms</code> data set. Use <code><a href="../reference/mo_property.html">mo_snomed()</a></code> to retrieve SNOMED codes, or use a SNOMED code for input in any <code>mo_*</code> function:</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/mo_property.html">mo_snomed</a></span><span class="op">(</span><span class="st">"S. aureus"</span><span class="op">)</span>
 <span class="co">#&gt; [1] 115329001   3092008 113961008</span>
@@ -852,7 +863,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </ul>
 </li>
 <li>Input values for <code><a href="../reference/as.disk.html">as.disk()</a></code> limited to a maximum of 50 millimeters</li>
-<li>Added a lifecycle state to every function, following <a href="https://www.tidyverse.org/lifecycle">the lifecycle circle of the <code>tidyverse</code></a>
+<li>Added a lifecycle state to every function, following the lifecycle circle of the <code>tidyverse</code>
 </li>
 <li>For in <code><a href="../reference/as.ab.html">as.ab()</a></code>: support for drugs starting with “co-” like co-amoxiclav, co-trimoxazole, co-trimazine and co-trimazole (thanks to Peter Dutey)</li>
 <li>Changes to the <code>antibiotics</code> data set (thanks to Peter Dutey):
@@ -886,11 +897,11 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>If you were dependent on the old Enterobacteriaceae family e.g. by using in your code:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_family</a></span><span class="op">(</span><span class="va">somebugs</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Enterobacteriaceae"</span><span class="op">)</span> <span class="va">...</span></code></pre></div>
 <p>then please adjust this to:</p>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">somebugs</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Enterobacterales"</span><span class="op">)</span> <span class="va">...</span></code></pre></div>
 </li>
@@ -904,7 +915,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Functions <code><a href="../reference/proportion.html">susceptibility()</a></code> and <code><a href="../reference/proportion.html">resistance()</a></code> as aliases of <code><a href="../reference/proportion.html">proportion_SI()</a></code> and <code><a href="../reference/proportion.html">proportion_R()</a></code>, respectively. These functions were added to make it more clear that “I” should be considered susceptible and not resistant.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
 <span class="va">example_isolates</span> <span class="op">%&gt;%</span>
@@ -933,7 +944,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>More intelligent way of coping with some consonants like “l” and “r”</p></li>
 <li>
 <p>Added a score (a certainty percentage) to <code><a href="../reference/as.mo.html">mo_uncertainties()</a></code>, that is calculated using the <a href="https://en.wikipedia.org/wiki/Levenshtein_distance">Levenshtein distance</a>:</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Stafylococcus aureus"</span>,
         <span class="st">"staphylokok aureuz"</span><span class="op">)</span><span class="op">)</span>
@@ -992,14 +1003,14 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Determination of first isolates now <strong>excludes</strong> all ‘unknown’ microorganisms at default, i.e. microbial code <code>"UNKNOWN"</code>. They can be included with the new argument <code>include_unknown</code>:</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span><span class="va">...</span>, include_unknown <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
 <p>For WHONET users, this means that all records/isolates with organism code <code>"con"</code> (<em>contamination</em>) will be excluded at default, since <code>as.mo("con") = "UNKNOWN"</code>. The function always shows a note with the number of ‘unknown’ microorganisms that were included or excluded.</p>
 </li>
 <li>
 <p>For code consistency, classes <code>ab</code> and <code>mo</code> will now be preserved in any subsetting or assignment. For the sake of data integrity, this means that invalid assignments will now result in <code>NA</code>:</p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># how it works in base R:</span>
 <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html">factor</a></span><span class="op">(</span><span class="st">"A"</span><span class="op">)</span>
@@ -1024,7 +1035,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Function <code><a href="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> to quickly get a <code>data.frame</code> with the results of all bug-drug combinations in a data set. The column containing microorganism codes is guessed automatically and its input is transformed with <code><a href="../reference/mo_property.html">mo_shortname()</a></code> at default:</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/bug_drug_combinations.html">bug_drug_combinations</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span>
 <span class="co">#&gt; NOTE: Using column `mo` as input for `col_mo`.</span>
@@ -1047,13 +1058,13 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <span class="co">#&gt; 4 Gram-negative AMX 227  0 405   632</span>
 <span class="co">#&gt; NOTE: Use 'format()' on this result to get a publicable/printable format.</span></code></pre></div>
 <p>You can format this to a printable format, ready for reporting or exporting to e.g. Excel with the base R <code><a href="https://rdrr.io/r/base/format.html">format()</a></code> function:</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="https://rdrr.io/r/base/format.html">format</a></span><span class="op">(</span><span class="va">x</span>, combine_IR <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div>
 </li>
 <li>
 <p>Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for <code>portion_*</code> functions or <code>count_*</code> functions. This can be used to determine the empiric susceptibility of a combination therapy. A new argument <code>only_all_tested</code> (<strong>which defaults to <code>FALSE</code></strong>) replaces the old <code>also_single_tested</code> and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the <code>portion</code> and <code>count</code> help pages), where the %SI is being determined:</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># --------------------------------------------------------------------</span>
 <span class="co">#                     only_all_tested = FALSE  only_all_tested = TRUE</span>
@@ -1075,7 +1086,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p><code>tibble</code> printing support for classes <code>rsi</code>, <code>mic</code>, <code>disk</code>, <code>ab</code> <code>mo</code>. When using <code>tibble</code>s containing antimicrobial columns, values <code>S</code> will print in green, values <code>I</code> will print in yellow and values <code>R</code> will print in red. Microbial IDs (class <code>mo</code>) will emphasise on the genus and species, not on the kingdom.</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># (run this on your own console, as this page does not support colour printing)</span>
 <span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org">dplyr</a></span><span class="op">)</span>
@@ -1158,7 +1169,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Function <code><a href="../reference/proportion.html">rsi_df()</a></code> to transform a <code>data.frame</code> to a data set containing only the microbial interpretation (S, I, R), the antibiotic, the percentage of S/I/R and the number of available isolates. This is a convenient combination of the existing functions <code><a href="../reference/count.html">count_df()</a></code> and <code>portion_df()</code> to immediately show resistance percentages and number of available isolates:</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span>
   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">AMX</span>, <span class="va">CIP</span><span class="op">)</span> <span class="op">%&gt;%</span>
@@ -1185,7 +1196,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li>UPEC (Uropathogenic <em>E. coli</em>)</li>
 </ul>
 <p>All these lead to the microbial ID of <em>E. coli</em>:</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"UPEC"</span><span class="op">)</span>
 <span class="co"># B_ESCHR_COL</span>
@@ -1290,7 +1301,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>when all values are unique it now shows a message instead of a warning</p></li>
 <li>
 <p>support for boxplots:</p>
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> 
   <span class="fu">freq</span><span class="op">(</span><span class="va">age</span><span class="op">)</span> <span class="op">%&gt;%</span> 
@@ -1385,7 +1396,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:</p>
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/filter_ab_class.html">filter_aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>
 <span class="fu"><a href="../reference/filter_ab_class.html">filter_carbapenems</a></span><span class="op">(</span><span class="op">)</span>
@@ -1399,7 +1410,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <span class="fu"><a href="../reference/filter_ab_class.html">filter_macrolides</a></span><span class="op">(</span><span class="op">)</span>
 <span class="fu"><a href="../reference/filter_ab_class.html">filter_tetracyclines</a></span><span class="op">(</span><span class="op">)</span></code></pre></div>
 <p>The <code>antibiotics</code> data set will be searched, after which the input data will be checked for column names with a value in any abbreviations, codes or official names found in the <code>antibiotics</code> data set. For example:</p>
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/filter_ab_class.html">filter_glycopeptides</a></span><span class="op">(</span>result <span class="op">=</span> <span class="st">"R"</span><span class="op">)</span>
 <span class="co"># Filtering on glycopeptide antibacterials: any of `vanc` or `teic` is R</span>
@@ -1408,7 +1419,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>All <code>ab_*</code> functions are deprecated and replaced by <code>atc_*</code> functions:</p>
-<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">ab_property</span> <span class="op">-&gt;</span> <span class="fu">atc_property</span><span class="op">(</span><span class="op">)</span>
 <span class="va">ab_name</span> <span class="op">-&gt;</span> <span class="fu">atc_name</span><span class="op">(</span><span class="op">)</span>
@@ -1429,7 +1440,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>New function <code><a href="../reference/age_groups.html">age_groups()</a></code> to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic AMR data analysis per age group.</p></li>
 <li>
 <p>New function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> as well as the base R <code><a href="../reference/plot.html">plot()</a></code> function can now be used for resistance prediction calculated with <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>:</p>
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span><span class="va">septic_patients</span>, col_ab <span class="op">=</span> <span class="st">"amox"</span><span class="op">)</span>
 <span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span>
@@ -1437,13 +1448,13 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>Functions <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> and <code><a href="../reference/first_isolate.html">filter_first_weighted_isolate()</a></code> to shorten and fasten filtering on data sets with antimicrobial results, e.g.:</p>
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/first_isolate.html">filter_first_isolate</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span>
 <span class="co"># or</span>
 <span class="fu"><a href="../reference/first_isolate.html">filter_first_isolate</a></span><span class="op">(</span><span class="va">septic_patients</span>, <span class="va">...</span><span class="op">)</span></code></pre></div>
 <p>is equal to:</p>
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span>
   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>only_firsts <span class="op">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span><span class="va">septic_patients</span>, <span class="va">...</span><span class="op">)</span><span class="op">)</span> <span class="op">%&gt;%</span>
@@ -1476,7 +1487,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Now handles incorrect spelling, like <code>i</code> instead of <code>y</code> and <code>f</code> instead of <code>ph</code>:</p>
-<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># mo_fullname() uses as.mo() internally</span>
 
@@ -1488,7 +1499,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>Uncertainty of the algorithm is now divided into four levels, 0 to 3, where the default <code>allow_uncertain = TRUE</code> is equal to uncertainty level 2. Run <code><a href="../reference/as.mo.html">?as.mo</a></code> for more info about these levels.</p>
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># equal:</span>
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">...</span>, allow_uncertain <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
@@ -1503,7 +1514,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>All microbial IDs that found are now saved to a local file <code>~/.Rhistory_mo</code>. Use the new function <code>clean_mo_history()</code> to delete this file, which resets the algorithms.</p></li>
 <li>
 <p>Incoercible results will now be considered ‘unknown’, MO code <code>UNKNOWN</code>. On foreign systems, properties of these will be translated to all languages already previously supported: German, Dutch, French, Italian, Spanish and Portuguese:</p>
-<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"qwerty"</span>, language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span>
 <span class="co"># Warning: </span>
@@ -1553,7 +1564,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:</p>
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="co"># Determine genus of microorganisms (mo) in `septic_patients` data set:</span>
 <span class="co"># OLD WAY</span>
@@ -1637,7 +1648,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Fewer than 3 characters as input for <code>as.mo</code> will return NA</p></li>
 <li>
 <p>Function <code>as.mo</code> (and all <code>mo_*</code> wrappers) now supports genus abbreviations with “species” attached</p>
-<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"E. species"</span><span class="op">)</span>        <span class="co"># B_ESCHR</span>
 <span class="fu"><a href="../reference/mo_property.html">mo_fullname</a></span><span class="op">(</span><span class="st">"E. spp."</span><span class="op">)</span>     <span class="co"># "Escherichia species"</span>
@@ -1654,7 +1665,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <ul>
 <li>
 <p>Support for grouping variables, test with:</p>
-<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> 
   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">hospital_id</span><span class="op">)</span> <span class="op">%&gt;%</span> 
@@ -1662,7 +1673,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 <li>
 <p>Support for (un)selecting columns:</p>
-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> 
   <span class="fu">freq</span><span class="op">(</span><span class="va">hospital_id</span><span class="op">)</span> <span class="op">%&gt;%</span> 
@@ -1742,7 +1753,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 </li>
 </ul>
 <p>They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:</p>
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="st">"E. coli"</span><span class="op">)</span>
 <span class="co"># [1] "Gram negative"</span>
@@ -1753,7 +1764,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <span class="fu"><a href="../reference/mo_property.html">mo_fullname</a></span><span class="op">(</span><span class="st">"S. group A"</span>, language <span class="op">=</span> <span class="st">"pt"</span><span class="op">)</span> <span class="co"># Portuguese</span>
 <span class="co"># [1] "Streptococcus grupo A"</span></code></pre></div>
 <p>Furthermore, former taxonomic names will give a note about the current taxonomic name:</p>
-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="st">"Esc blattae"</span><span class="op">)</span>
 <span class="co"># Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)</span>
@@ -1768,7 +1779,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Function <code>is.rsi.eligible</code> to check for columns that have valid antimicrobial results, but do not have the <code>rsi</code> class yet. Transform the columns of your raw data with: <code>data %&gt;% mutate_if(is.rsi.eligible, as.rsi)</code></p></li>
 <li>
 <p>Functions <code>as.mo</code> and <code>is.mo</code> as replacements for <code>as.bactid</code> and <code>is.bactid</code> (since the <code>microoganisms</code> data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The <code>as.mo</code> function determines microbial IDs using intelligent rules:</p>
-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"E. coli"</span><span class="op">)</span>
 <span class="co"># [1] B_ESCHR_COL</span>
@@ -1777,7 +1788,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"S group A"</span><span class="op">)</span>
 <span class="co"># [1] B_STRPTC_GRA</span></code></pre></div>
 <p>And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:</p>
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">thousands_of_E_colis</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="st">"E. coli"</span>, <span class="fl">25000</span><span class="op">)</span>
 <span class="fu">microbenchmark</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/microbenchmark/man/microbenchmark.html">microbenchmark</a></span><span class="op">(</span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">thousands_of_E_colis</span><span class="op">)</span>, unit <span class="op">=</span> <span class="st">"s"</span><span class="op">)</span>
@@ -1811,7 +1822,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Added three antimicrobial agents to the <code>antibiotics</code> data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)</p></li>
 <li>
 <p>Added 163 trade names to the <code>antibiotics</code> data set, it now contains 298 different trade names in total, e.g.:</p>
-<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="fu">ab_official</span><span class="op">(</span><span class="st">"Bactroban"</span><span class="op">)</span>
 <span class="co"># [1] "Mupirocin"</span>
@@ -1828,7 +1839,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Added arguments <code>minimum</code> and <code>as_percent</code> to <code>portion_df</code></p></li>
 <li>
 <p>Support for quasiquotation in the functions series <code>count_*</code> and <code>portions_*</code>, and <code>n_rsi</code>. This allows to check for more than 2 vectors or columns.</p>
-<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">septic_patients</span> <span class="op">%&gt;%</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="va">amox</span>, <span class="va">cipr</span><span class="op">)</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/count.html">count_IR</a></span><span class="op">(</span><span class="op">)</span>
 <span class="co"># which is the same as:</span>
@@ -1848,12 +1859,12 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
 <li><p>Added longest en shortest character length in the frequency table (<code>freq</code>) header of class <code>character</code></p></li>
 <li>
 <p>Support for types (classes) list and matrix for <code>freq</code></p>
-<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">my_matrix</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/with.html">with</a></span><span class="op">(</span><span class="va">septic_patients</span>, <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="va">age</span>, <span class="va">gender</span><span class="op">)</span>, ncol <span class="op">=</span> <span class="fl">2</span><span class="op">)</span><span class="op">)</span>
 <span class="fu">freq</span><span class="op">(</span><span class="va">my_matrix</span><span class="op">)</span></code></pre></div>
 <p>For lists, subsetting is possible:</p>
-<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r">
 <code class="sourceCode R">
 <span class="va">my_list</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>age <span class="op">=</span> <span class="va">septic_patients</span><span class="op">$</span><span class="va">age</span>, gender <span class="op">=</span> <span class="va">septic_patients</span><span class="op">$</span><span class="va">gender</span><span class="op">)</span>
 <span class="va">my_list</span> <span class="op">%&gt;%</span> <span class="fu">freq</span><span class="op">(</span><span class="va">age</span><span class="op">)</span>

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: 2021-03-05T14:27Z
+last_built: 2021-03-14T08:55Z
 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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9032</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">1.5.0.9016</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -301,12 +301,13 @@
 <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="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
 
-<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>
+<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>
     <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/antibiotic_class_selectors.html

@@ -83,7 +83,7 @@
       </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">1.5.0.9021</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -283,7 +283,7 @@
     </tr>
     <tr>
       <th>only_rsi_columns</th>
-      <td><p>a logical to indicate whether only columns of class <a href='[rsi]'><code>&lt;rsi&gt;</code></a> must be selected (defaults to <code>FALSE</code>)</p></td>
+      <td><p>a logical to indicate whether only columns of class <code>&lt;rsi&gt;</code> must be selected (defaults to <code>FALSE</code>), see <code><a href='as.rsi.html'>as.rsi()</a></code></p></td>
     </tr>
     </table>
 

docs/reference/as.mo.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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -372,7 +372,9 @@
 <li><p>Becker K <em>et al.</em> <strong>Implications of identifying the recently defined members of the <em>S. aureus</em> complex, <em>S. argenteus</em> and <em>S. schweitzeri</em>: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).</strong> 2019. Clin Microbiol Infect; doi: <a href='https://doi.org/10.1016/j.cmi.2019.02.028'>10.1016/j.cmi.2019.02.028</a></p></li>
 <li><p>Becker K <em>et al.</em> <strong>Emergence of coagulase-negative staphylococci</strong> 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi: <a href='https://doi.org/10.1080/14787210.2020.1730813'>10.1080/14787210.2020.1730813</a></p></li>
 <li><p>Lancefield RC <strong>A serological differentiation of human and other groups of hemolytic streptococci</strong>. 1933. J Exp Med. 57(4): 571–95; doi: <a href='https://doi.org/10.1084/jem.57.4.571'>10.1084/jem.57.4.571</a></p></li>
-<li><p>Catalogue of Life: Annual Checklist (public online taxonomic database), <a href='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a> (check included annual version with <code><a href='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p></li>
+<li><p>Catalogue of Life: 2019 Annual Checklist, <a href='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a></p></li>
+<li><p>List of Prokaryotic names with Standing in Nomenclature (March 2021), doi: <a href='https://doi.org/10.1099/ijsem.0.004332'>10.1099/ijsem.0.004332</a></p></li>
+<li><p>US Edition of SNOMED CT from 1 September 2020, retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: <a href='https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009'>https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009</a></p></li>
 </ol>
 
     <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
@@ -397,7 +399,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
 <li><p><i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.</p></li>
 </ul>
 
-<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>,\<em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
+<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>, <em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
 <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., <code>"E. coli"</code> will return the microbial ID of <em>Escherichia coli</em> (\(m = 0.688\), a highly prevalent microorganism found in humans) and not <em>Entamoeba coli</em> (\(m = 0.079\), a less prevalent microorganism in humans), although the latter would alphabetically come first.</p>
     <h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2>
 

docs/reference/filter_ab_class.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">1.5.0.9019</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -384,7 +384,7 @@
     </tr>
     <tr>
       <th>only_rsi_columns</th>
-      <td><p>a logical to indicate whether only columns must be included that were <a href='[rsi]'>transformed to class <code>&lt;rsi&gt;</code></a> on beforehand (defaults to <code>FALSE</code>)</p></td>
+      <td><p>a logical to indicate whether only columns must be included that were transformed to class <code>&lt;rsi&gt;</code> (see <code><a href='as.rsi.html'>as.rsi()</a></code>) on beforehand (defaults to <code>FALSE</code>)</p></td>
     </tr>
     <tr>
       <th>...</th>

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">1.5.0.9019</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -260,6 +260,7 @@
   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>
 
@@ -325,7 +326,7 @@
     </tr>
     <tr>
       <th>icu_exclude</th>
-      <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>
+      <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>
     </tr>
     <tr>
       <th>specimen_group</th>
@@ -337,7 +338,7 @@
     </tr>
     <tr>
       <th>ignore_I</th>
-      <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>
+      <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>
     </tr>
     <tr>
       <th>points_threshold</th>
@@ -349,7 +350,11 @@
     </tr>
     <tr>
       <th>include_unknown</th>
-      <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>
+      <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>
     </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">1.5.0.9013</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -383,13 +383,14 @@
 
     <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="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <p>The colours for labels and points can be changed by adding another scale layer for colour, such as <code><a href='https://ggplot2.tidyverse.org/reference/scale_viridis.html'>scale_colour_viridis_d()</a></code> and <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>
 
     
 
-<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>
+<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>
 
     <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>
@@ -409,7 +410,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing<
   <span class='co'># new </span>
   <span class='fu'>ggplot_pca</span><span class='op'>(</span><span class='va'>pca_model</span><span class='op'>)</span>
   
-  <span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='http://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
+  <span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
     <span class='fu'>ggplot_pca</span><span class='op'>(</span><span class='va'>pca_model</span><span class='op'>)</span> <span class='op'>+</span>
       <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/scale_viridis.html'>scale_colour_viridis_d</a></span><span class='op'>(</span><span class='op'>)</span> <span class='op'>+</span>
       <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/labs.html'>labs</a></span><span class='op'>(</span>title <span class='op'>=</span> <span class='st'>"Title here"</span><span class='op'>)</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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -415,12 +415,13 @@
 <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="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
 
-<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>
+<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>
     <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>
 
     
@@ -428,7 +429,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing<
 <p>On our website <a href='https://msberends.github.io/AMR/'>https://msberends.github.io/AMR/</a> you can find <a href='https://msberends.github.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR data analysis, the <a href='https://msberends.github.io/AMR/reference/'>complete documentation of all functions</a> and <a href='https://msberends.github.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>. As we would like to better understand the backgrounds and needs of our users, please <a href='https://msberends.github.io/AMR/survey.html'>participate in our survey</a>!</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='http://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span> <span class='op'>&amp;</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
+    <pre class="examples"><span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span> <span class='op'>&amp;</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
  
   <span class='co'># get antimicrobial results for drugs against a UTI:</span>
   <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span><span class='op'>(</span><span class='va'>example_isolates</span> <span class='op'>%&gt;%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>select</a></span><span class='op'>(</span><span class='va'>AMX</span>, <span class='va'>NIT</span>, <span class='va'>FOS</span>, <span class='va'>TMP</span>, <span class='va'>CIP</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>+</span>

docs/reference/guess_ab_col.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">1.5.0.9019</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -266,7 +266,7 @@
     </tr>
     <tr>
       <th>only_rsi_columns</th>
-      <td><p>a logical to indicate whether only antibiotic columns must be detected that were <a href='[rsi]'>transformed to class <code>&lt;rsi&gt;</code></a> on beforehand (defaults to <code>FALSE</code>)</p></td>
+      <td><p>a logical to indicate whether only antibiotic columns must be detected that were transformed to class <code>&lt;rsi&gt;</code> (see <code><a href='as.rsi.html'>as.rsi()</a></code>) on beforehand (defaults to <code>FALSE</code>)</p></td>
     </tr>
     </table>
 

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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -326,7 +326,7 @@
         <td>
           <p><code><a href="lifecycle.html">lifecycle</a></code> </p>
         </td>
-        <td><p>Lifecycles of Functions in the <code>amr</code> Package</p></td>
+        <td><p>Lifecycles of Functions in the <code>AMR</code> Package</p></td>
       </tr><tr>
         
         <td>
@@ -450,12 +450,6 @@
           <p><code><a href="eucast_rules.html">eucast_rules()</a></code> <code><a href="eucast_rules.html">eucast_dosage()</a></code> </p>
         </td>
         <td><p>Apply EUCAST Rules</p></td>
-      </tr><tr>
-        
-        <td>
-          <p><code><a href="isolate_identifier.html">isolate_identifier()</a></code> <code><a href="isolate_identifier.html">all.equal(<i>&lt;isolate_identifier&gt;</i>)</a></code> </p>
-        </td>
-        <td><p>Create Identifier of an Isolate</p></td>
       </tr>
     </tbody><tbody>
       <tr>

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">1.5.0.9019</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -317,7 +317,7 @@
     </tr>
     <tr>
       <th>ignore_I</th>
-      <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>
+      <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>
     </tr>
     <tr>
       <th>points_threshold</th>

docs/reference/lifecycle.html

@@ -6,7 +6,7 @@
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
 
-<title>Lifecycles of Functions in the amr Package — lifecycle • AMR (for R)</title>
+<title>Lifecycles of Functions in the AMR Package — lifecycle • AMR (for R)</title>
 
 <!-- favicons -->
 <link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png">
@@ -48,7 +48,7 @@
   <link href="../extra.css" rel="stylesheet">
   <script src="../extra.js"></script>
 
-<meta property="og:title" content="Lifecycles of Functions in the amr Package — lifecycle" />
+<meta property="og:title" content="Lifecycles of Functions in the AMR Package — lifecycle" />
 <meta property="og:description" content="Functions in this AMR package are categorised using the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle.
  
 This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions." />
@@ -84,7 +84,7 @@ This page contains a section for every lifecycle (with text borrowed from the af
       </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">1.5.0.9013</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -235,13 +235,13 @@ This page contains a section for every lifecycle (with text borrowed from the af
 <div class="row">
   <div class="col-md-9 contents">
     <div class="page-header">
-    <h1>Lifecycles of Functions in the <code>amr</code> Package</h1>
+    <h1>Lifecycles of Functions in the <code>AMR</code> Package</h1>
     <small class="dont-index">Source: <a href='https://github.com/msberends/AMR/blob/master/R/lifecycle.R'><code>R/lifecycle.R</code></a></small>
     <div class="hidden name"><code>lifecycle.Rd</code></div>
     </div>
 
     <div class="ref-description">
-    <p>Functions in this <code>AMR</code> package are categorised using <a href='https://www.Tidyverse.org/lifecycle'>the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle</a>.</p>
+    <p>Functions in this <code>AMR</code> package are categorised using <a href='https://lifecycle.r-lib.org/articles/stages.html'>the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle</a>.</p>
 <p><img src='figures/lifecycle_tidyverse.svg' height=200px style=margin-bottom:5px /> <br />
 This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions.</p>
     </div>

docs/reference/microorganisms.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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -256,20 +256,24 @@
 <li><p><code>species_id</code><br /> ID of the species as used by the Catalogue of Life</p></li>
 <li><p><code>source</code><br /> Either "CoL", "LPSN" or "manually added" (see <em>Source</em>)</p></li>
 <li><p><code>prevalence</code><br /> Prevalence of the microorganism, see <code><a href='as.mo.html'>as.mo()</a></code></p></li>
-<li><p><code>snomed</code><br /> SNOMED code of the microorganism. Use <code><a href='mo_property.html'>mo_snomed()</a></code> to retrieve it quickly, see <code><a href='mo_property.html'>mo_property()</a></code>.</p></li>
+<li><p><code>snomed</code><br /> Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, according to the US Edition of SNOMED CT from 1 September 2020 (see <em>Source</em>). Use <code><a href='mo_property.html'>mo_snomed()</a></code> to retrieve it quickly, see <code><a href='mo_property.html'>mo_property()</a></code>.</p></li>
 </ul>
 
     <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
 
-    <p>Catalogue of Life: 2019 Annual Checklist</p><ul>
+    <p>Catalogue of Life: 2019 Annual Checklist as currently implemented in this <code>AMR</code> package:</p><ul>
 <li><p>Annual Checklist (public online taxonomic database), <a href='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a></p></li>
 </ul>
 
-<p>List of Prokaryotic names with Standing in Nomenclature: March 2021</p><ul>
+<p>List of Prokaryotic names with Standing in Nomenclature (March 2021) as currently implemented in this <code>AMR</code> package:</p><ul>
 <li><p>Parte, A.C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Goker, M. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; doi: <a href='https://doi.org/10.1099/ijsem.0.004332'>10.1099/ijsem.0.004332</a></p></li>
 <li><p>Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: <a href='https://doi.org/10.1099/ijsem.0.002786'>10.1099/ijsem.0.002786</a></p></li>
 <li><p>Parte, A.C. (2014). LPSN — List of Prokaryotic names with Standing in Nomenclature. Nucleic Acids Research, 42, Issue D1, D613–D616; doi: <a href='https://doi.org/10.1093/nar/gkt1111'>10.1093/nar/gkt1111</a></p></li>
 <li><p>Euzeby, J.P. (1997). List of Bacterial Names with Standing in Nomenclature: a Folder Available on the Internet. International Journal of Systematic Bacteriology, 47, 590-592; doi: <a href='https://doi.org/10.1099/00207713-47-2-590'>10.1099/00207713-47-2-590</a></p></li>
+</ul>
+
+<p>US Edition of SNOMED CT from 1 September 2020 as currently implemented in this <code>AMR</code> package:</p><ul>
+<li><p>Retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: <a href='https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009'>https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009</a></p></li>
 </ul>
 
     <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>

docs/reference/mo_matching_score.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">1.5.0.9016</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -272,7 +272,7 @@
 <li><p><i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.</p></li>
 </ul>
 
-<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>,\<em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
+<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>, <em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
 <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., <code>"E. coli"</code> will return the microbial ID of <em>Escherichia coli</em> (\(m = 0.688\), a highly prevalent microorganism found in humans) and not <em>Entamoeba coli</em> (\(m = 0.079\), a less prevalent microorganism in humans), although the latter would alphabetically come first.</p>
     <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 

docs/reference/mo_property.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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 
@@ -334,7 +334,7 @@
 <li><p>An <a href='https://rdrr.io/r/base/integer.html'>integer</a> in case of <code>mo_year()</code></p></li>
 <li><p>A <a href='https://rdrr.io/r/base/list.html'>list</a> in case of <code>mo_taxonomy()</code> and <code>mo_info()</code></p></li>
 <li><p>A named <a href='https://rdrr.io/r/base/character.html'>character</a> in case of <code>mo_url()</code></p></li>
-<li><p>A <a href='https://rdrr.io/r/base/double.html'>double</a> in case of <code>mo_snomed()</code></p></li>
+<li><p>A <a href='https://rdrr.io/r/base/numeric.html'>numeric</a> in case of <code>mo_snomed()</code></p></li>
 <li><p>A <a href='https://rdrr.io/r/base/character.html'>character</a> in all other cases</p></li>
 </ul>
 
@@ -353,6 +353,7 @@
 <p>Intrinsic resistance - <code>mo_is_intrinsic_resistant()</code> - will be determined based on the <a href='intrinsic_resistant.html'>intrinsic_resistant</a> data set, which is based on <a href='https://www.eucast.org/expert_rules_and_intrinsic_resistance/'>'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.2</a> (2020). The <code>mo_is_intrinsic_resistant()</code> functions can be vectorised over arguments <code>x</code> (input for microorganisms) and over <code>ab</code> (input for antibiotics).</p>
 <p>All output <a href='translate.html'>will be translated</a> where possible.</p>
 <p>The function <code>mo_url()</code> will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.</p>
+<p>SNOMED codes - <code>mo_snomed()</code> - are from the US Edition of SNOMED CT from 1 September 2020. See the <a href='microorganisms.html'>microorganisms</a> data set for more info.</p>
     <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
@@ -375,7 +376,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
 <li><p><i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.</p></li>
 </ul>
 
-<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>,\<em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
+<p>The grouping into human pathogenic prevalence (\(p\)) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. <strong>Group 1</strong> (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is <em>Enterococcus</em>, <em>Staphylococcus</em> or <em>Streptococcus</em>. This group consequently contains all common Gram-negative bacteria, such as <em>Pseudomonas</em> and <em>Legionella</em> and all species within the order Enterobacterales. <strong>Group 2</strong> consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is <em>Absidia</em>, <em>Acremonium</em>, <em>Actinotignum</em>, <em>Alternaria</em>, <em>Anaerosalibacter</em>, <em>Apophysomyces</em>, <em>Arachnia</em>, <em>Aspergillus</em>, <em>Aureobacterium</em>, <em>Aureobasidium</em>, <em>Bacteroides</em>, <em>Basidiobolus</em>, <em>Beauveria</em>, <em>Blastocystis</em>, <em>Branhamella</em>, <em>Calymmatobacterium</em>, <em>Candida</em>, <em>Capnocytophaga</em>, <em>Catabacter</em>, <em>Chaetomium</em>, <em>Chryseobacterium</em>, <em>Chryseomonas</em>, <em>Chrysonilia</em>, <em>Cladophialophora</em>, <em>Cladosporium</em>, <em>Conidiobolus</em>, <em>Cryptococcus</em>, <em>Curvularia</em>, <em>Exophiala</em>, <em>Exserohilum</em>, <em>Flavobacterium</em>, <em>Fonsecaea</em>, <em>Fusarium</em>, <em>Fusobacterium</em>, <em>Hendersonula</em>, <em>Hypomyces</em>, <em>Koserella</em>, <em>Lelliottia</em>, <em>Leptosphaeria</em>, <em>Leptotrichia</em>, <em>Malassezia</em>, <em>Malbranchea</em>, <em>Mortierella</em>, <em>Mucor</em>, <em>Mycocentrospora</em>, <em>Mycoplasma</em>, <em>Nectria</em>, <em>Ochroconis</em>, <em>Oidiodendron</em>, <em>Phoma</em>, <em>Piedraia</em>, <em>Pithomyces</em>, <em>Pityrosporum</em>, <em>Prevotella</em>, <em>Pseudallescheria</em>, <em>Rhizomucor</em>, <em>Rhizopus</em>, <em>Rhodotorula</em>, <em>Scolecobasidium</em>, <em>Scopulariopsis</em>, <em>Scytalidium</em>,<em>Sporobolomyces</em>, <em>Stachybotrys</em>, <em>Stomatococcus</em>, <em>Treponema</em>, <em>Trichoderma</em>, <em>Trichophyton</em>, <em>Trichosporon</em>, <em>Tritirachium</em> or <em>Ureaplasma</em>. <strong>Group 3</strong> consists of all other microorganisms.</p>
 <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., <code>"E. coli"</code> will return the microbial ID of <em>Escherichia coli</em> (\(m = 0.688\), a highly prevalent microorganism found in humans) and not <em>Entamoeba coli</em> (\(m = 0.079\), a less prevalent microorganism in humans), although the latter would alphabetically come first.</p>
     <h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2>
 
@@ -393,7 +394,9 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 <li><p>Becker K <em>et al.</em> <strong>Implications of identifying the recently defined members of the <em>S. aureus</em> complex, <em>S. argenteus</em> and <em>S. schweitzeri</em>: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).</strong> 2019. Clin Microbiol Infect; doi: <a href='https://doi.org/10.1016/j.cmi.2019.02.028'>10.1016/j.cmi.2019.02.028</a></p></li>
 <li><p>Becker K <em>et al.</em> <strong>Emergence of coagulase-negative staphylococci</strong> 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi: <a href='https://doi.org/10.1080/14787210.2020.1730813'>10.1080/14787210.2020.1730813</a></p></li>
 <li><p>Lancefield RC <strong>A serological differentiation of human and other groups of hemolytic streptococci</strong>. 1933. J Exp Med. 57(4): 571–95; doi: <a href='https://doi.org/10.1084/jem.57.4.571'>10.1084/jem.57.4.571</a></p></li>
-<li><p>Catalogue of Life: Annual Checklist (public online taxonomic database), <a href='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a> (check included annual version with <code><a href='catalogue_of_life_version.html'>catalogue_of_life_version()</a></code>).</p></li>
+<li><p>Catalogue of Life: 2019 Annual Checklist, <a href='http://www.catalogueoflife.org'>http://www.catalogueoflife.org</a></p></li>
+<li><p>List of Prokaryotic names with Standing in Nomenclature (March 2021), doi: <a href='https://doi.org/10.1099/ijsem.0.004332'>10.1099/ijsem.0.004332</a></p></li>
+<li><p>US Edition of SNOMED CT from 1 September 2020, retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: <a href='https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009'>https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009</a></p></li>
 </ol>
 
     <h2 class="hasAnchor" id="reference-data-publicly-available"><a class="anchor" href="#reference-data-publicly-available"></a>Reference Data Publicly Available</h2>
@@ -505,7 +508,8 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
 <span class='co'># get a list with the complete taxonomy (from kingdom to subspecies)</span>
 <span class='fu'>mo_taxonomy</span><span class='op'>(</span><span class='st'>"E. coli"</span><span class='op'>)</span>
-<span class='co'># get a list with the taxonomy, the authors, Gram-stain and URL to the online database</span>
+<span class='co'># get a list with the taxonomy, the authors, Gram-stain,</span>
+<span class='co'>#   SNOMED codes, and URL to the online database</span>
 <span class='fu'>mo_info</span><span class='op'>(</span><span class='st'>"E. coli"</span><span class='op'>)</span>
 <span class='co'># }</span>
 </pre>

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">1.5.0.9016</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -311,12 +311,13 @@
 
     <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="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
 
-<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>
+<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>
     <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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -280,12 +280,13 @@
 
     <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="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
 
-<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>
+<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>
     <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">1.5.0.9019</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.5.0.9040</span>
       </span>
     </div>
 
@@ -364,12 +364,13 @@
 <li><p><code>"lin"</code> or <code>"linear"</code>: a linear regression model</p></li>
 </ul>
 
-    <h2 class="hasAnchor" id="maturing-lifecycle"><a class="anchor" href="#maturing-lifecycle"></a>Maturing Lifecycle</h2>
+    <h2 class="hasAnchor" id="stable-lifecycle"><a class="anchor" href="#stable-lifecycle"></a>Stable Lifecycle</h2>
 
     
 
-<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>
+<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>
     <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>
 
     
@@ -400,7 +401,7 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
                         year_min <span class='op'>=</span> <span class='fl'>2010</span>,
                         model <span class='op'>=</span> <span class='st'>"binomial"</span><span class='op'>)</span>
 <span class='fu'><a href='plot.html'>plot</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>
-<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='http://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
+<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
   <span class='fu'>ggplot_rsi_predict</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span>
 <span class='op'>}</span>
 
@@ -418,7 +419,7 @@ A microorganism is categorised as <em>Susceptible, Increased exposure</em> when
 <span class='op'>}</span>
 
 <span class='co'># create nice plots with ggplot2 yourself</span>
-<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span> <span class='op'>&amp;</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='http://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
+<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span> <span class='op'>&amp;</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://ggplot2.tidyverse.org'>"ggplot2"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
 
   <span class='va'>data</span> <span class='op'>&lt;-</span> <span class='va'>example_isolates</span> <span class='op'>%&gt;%</span>
     <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span><span class='op'>(</span><span class='va'>mo</span> <span class='op'>==</span> <span class='fu'><a href='as.mo.html'>as.mo</a></span><span class='op'>(</span><span class='st'>"E. coli"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>%&gt;%</span>

docs/sitemap.xml

@@ -102,9 +102,6 @@
   <url>
     <loc>https://msberends.github.io/AMR//reference/intrinsic_resistant.html</loc>
   </url>
-  <url>
-    <loc>https://msberends.github.io/AMR//reference/isolate_identifier.html</loc>
-  </url>
   <url>
     <loc>https://msberends.github.io/AMR//reference/join.html</loc>
   </url>

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">1.5.0.9031</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.6.0</span>
       </span>
     </div>
 

git_premaster.sh

@@ -124,7 +124,8 @@ echo "••••••••••••••••••••"
 echo "• Building package •"
 echo "••••••••••••••••••••"
 echo "• Building 'data-raw/AMR_latest.tar.gz'..."
-Rscript -e "x <- devtools::build(path = 'data-raw/AMR_latest.tar.gz', vignettes = FALSE, manual = FALSE, binary = FALSE, quiet = TRUE)"
+Rscript -e "x <- devtools::build(path = 'data-raw', vignettes = FALSE, manual = FALSE, binary = FALSE, quiet = TRUE)"
+mv data-raw/AMR_*.tar.gz data-raw/AMR_latest.tar.gz
 echo "• Installing..."
 Rscript -e "devtools::install(quiet = TRUE, dependencies = FALSE)"
 echo

index.md

@@ -13,15 +13,15 @@
 
 `AMR` is a free, open-source and independent [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
 
-After installing this package, R knows [**~70,000 distinct microbial species**](./reference/microorganisms.html) and all [**~550 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+After installing this package, R knows [**~70,000 distinct microbial species**](./reference/microorganisms.html) and all [**~550 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
 
-This package is [fully independent of any other R package](https://en.wikipedia.org/wiki/Dependency_hell) and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package is [actively maintained](./news) and is free software (see [Copyright](#copyright)).
+This package is [fully independent of any other R package](https://en.wikipedia.org/wiki/Dependency_hell) and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package is [actively maintained](./news) and is free software (see [Copyright](#copyright)).
 
 <div class="main-content" style="display: inline-block;">
   <p>
     <a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a>
-    <strong>Used in 148 countries</strong><br>
-    Since its first public release in early 2018, this package has been downloaded from 148 countries. Click the map to enlarge and to see the country names.</p>
+    <strong>Used in 155 countries</strong><br>
+    Since its first public release in early 2018, this package has been downloaded from 155 countries. Click the map to enlarge and to see the country names.</p>
 </div>
 
 ##### With `AMR` (for R), there's always a knowledgeable microbiologist by your side!
@@ -75,7 +75,7 @@ The development of this package is part of, related to, or made possible by:
 <div align="center">
   <a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a>
   <a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a>
-  <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice"><img src="./logo_certe.png" class="partner_logo"></a>
+  <a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" class="partner_logo"></a>
   <a href="http://www.eurhealth-1health.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" class="partner_logo"></a>
   <a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" class="partner_logo"></a>
 </div>
@@ -98,7 +98,7 @@ This package can be used for:
   * Applying EUCAST expert rules ([manual](./reference/eucast_rules.html))
   * Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code ([manual](./reference/mo_property.html))
   * Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code ([manual](./reference/ab_property.html))
-  * Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI ([link](./articles/datasets.html))
+  * Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to R/SI ([link](./articles/datasets.html))
   * Principal component analysis for AMR ([tutorial](./articles/PCA.html))
 
 ### Get this package

man/ab_from_text.Rd

@@ -54,10 +54,12 @@ With using \code{collapse}, this function will return a \link{character}:\cr
 \code{df \%>\% mutate(abx = ab_from_text(clinical_text, collapse = "|"))}
 }
 }
-\section{Maturing Lifecycle}{
+\section{Stable Lifecycle}{
 
-\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')}.
+\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.
 }
 
 \section{Read more on Our Website!}{

man/antibiotic_class_selectors.Rd

@@ -52,7 +52,7 @@ tetracyclines(only_rsi_columns = FALSE)
 \arguments{
 \item{ab_class}{an antimicrobial class, like \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
 
-\item{only_rsi_columns}{a logical to indicate whether only columns of class \href{[rsi]}{\verb{<rsi>}} must be selected (defaults to \code{FALSE})}
+\item{only_rsi_columns}{a logical to indicate whether only columns of class \verb{<rsi>} must be selected (defaults to \code{FALSE}), see \code{\link[=as.rsi]{as.rsi()}}}
 }
 \description{
 These functions help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. \strong{\Sexpr{ifelse(as.double(R.Version()$major) + (as.double(R.Version()$minor) / 10) < 3.2, paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}

man/as.mic.Rd

@@ -11,18 +11,50 @@ as.mic(x, na.rm = FALSE)
 is.mic(x)
 }
 \arguments{
-\item{x}{vector}
+\item{x}{character or numeric vector}
 
 \item{na.rm}{a logical indicating whether missing values should be removed}
 }
 \value{
-Ordered \link{factor} with additional class \code{\link{mic}}
+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.
 }
 \description{
-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.
+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.
 }
 \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/as.mo.Rd

@@ -127,7 +127,9 @@ The intelligent rules consider the prevalence of microorganisms in humans groupe
 \item Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the \emph{S. aureus} complex, \emph{S. argenteus} and \emph{S. schweitzeri}: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).} 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
 \item Becker K \emph{et al.} \strong{Emergence of coagulase-negative staphylococci} 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
 \item Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95; \doi{10.1084/jem.57.4.571}
-\item Catalogue of Life: Annual Checklist (public online taxonomic database), \url{http://www.catalogueoflife.org} (check included annual version with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}).
+\item Catalogue of Life: 2019 Annual Checklist, \url{http://www.catalogueoflife.org}
+\item List of Prokaryotic names with Standing in Nomenclature (March 2021), \doi{10.1099/ijsem.0.004332}
+\item US Edition of SNOMED CT from 1 September 2020, retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: \url{https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009}
 }
 }
 
@@ -155,7 +157,7 @@ where:
 \item \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
 }
 
-The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella},\\\emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium},\emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
+The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella}, \emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium},\emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
 
 All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.079}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
 }

man/filter_ab_class.Rd

@@ -148,7 +148,7 @@ filter_tetracyclines(
 
 \item{scope}{the scope to check which variables to check, can be \code{"any"} (default) or \code{"all"}}
 
-\item{only_rsi_columns}{a logical to indicate whether only columns must be included that were \href{[rsi]}{transformed to class \verb{<rsi>}} on beforehand (defaults to \code{FALSE})}
+\item{only_rsi_columns}{a logical to indicate whether only columns must be included that were transformed to class \verb{<rsi>} (see \code{\link[=as.rsi]{as.rsi()}}) on beforehand (defaults to \code{FALSE})}
 
 \item{...}{arguments passed on to \code{\link[=filter_ab_class]{filter_ab_class()}}}
 }

man/first_isolate.Rd

@@ -29,6 +29,7 @@ first_isolate(
   points_threshold = 2,
   info = interactive(),
   include_unknown = FALSE,
+  include_untested_rsi = TRUE,
   ...
 )
 
@@ -70,19 +71,21 @@ filter_first_weighted_isolate(
 
 \item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
 
-\item{icu_exclude}{logical whether ICU isolates should be excluded (rows with value \code{TRUE} in the column set with \code{col_icu})}
+\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{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 determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, 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{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 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{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{...}{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

@@ -106,12 +106,14 @@ ggplot_pca(
 Produces a \code{ggplot2} variant of a so-called \href{https://en.wikipedia.org/wiki/Biplot}{biplot} for PCA (principal component analysis), but is more flexible and more appealing than the base \R \code{\link[=biplot]{biplot()}} function.
 }
 \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()}.
+The colours for labels and points can be changed by adding another scale layer for colour, such as \code{scale_colour_viridis_d()} and \code{scale_colour_brewer()}.
 }
-\section{Maturing Lifecycle}{
+\section{Stable Lifecycle}{
 
-\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')}.
+\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.
 }
 
 \examples{

man/ggplot_rsi.Rd

@@ -140,10 +140,12 @@ 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{Maturing Lifecycle}{
+\section{Stable Lifecycle}{
 
-\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')}.
+\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.
 }
 
 \section{Read more on Our Website!}{

man/guess_ab_col.Rd

@@ -18,7 +18,7 @@ guess_ab_col(
 
 \item{verbose}{a logical to indicate whether additional info should be printed}
 
-\item{only_rsi_columns}{a logical to indicate whether only antibiotic columns must be detected that were \href{[rsi]}{transformed to class \verb{<rsi>}} on beforehand (defaults to \code{FALSE})}
+\item{only_rsi_columns}{a logical to indicate whether only antibiotic columns must be detected that were transformed to class \verb{<rsi>} (see \code{\link[=as.rsi]{as.rsi()}}) on beforehand (defaults to \code{FALSE})}
 }
 \value{
 A column name of \code{x}, or \code{NULL} when no result is found.