From c5f00f4a9fcb7887b127fdeee795165cc419a007 Mon Sep 17 00:00:00 2001 From: "Matthijs S. Berends" Date: Thu, 28 Nov 2019 22:32:17 +0100 Subject: [PATCH] (v0.8.0.9036) complete documentation rewrite --- .lintr | 1 - DESCRIPTION | 7 +- NAMESPACE | 1 + NEWS.md | 6 +- R/ab.R | 13 +- R/ab_property.R | 27 +- R/age.R | 39 ++- R/amr.R | 37 ++- R/atc_online.R | 60 ++--- R/availability.R | 8 +- R/bug_drug_combinations.R | 18 +- R/catalogue_of_life.R | 34 ++- R/count.R | 20 +- R/data.R | 254 ++++++++---------- R/deprecated.R | 21 +- R/disk.R | 8 +- R/eucast_rules.R | 227 ++++++++-------- R/filter_ab_class.R | 8 +- R/first_isolate.R | 78 +++--- R/g.test.R | 70 ++--- R/ggplot_rsi.R | 42 +-- R/guess_ab_col.R | 10 +- R/join_microorganisms.R | 12 +- R/key_antibiotics.R | 51 ++-- R/kurtosis.R | 7 +- R/like.R | 10 +- R/mdro.R | 53 ++-- R/mic.R | 8 +- R/mo.R | 121 ++++----- R/mo_history.R | 20 +- R/mo_property.R | 44 ++- R/mo_source.R | 64 ++--- R/p_symbol.R | 6 +- R/proportion.R | 54 ++-- R/read.4d.R | 8 +- R/resistance_predict.R | 60 +++-- R/rsi.R | 34 ++- R/rsi_df.R | 3 +- R/skewness.R | 6 +- R/sysdata.rda | Bin 455532 -> 455514 bytes R/translate.R | 12 +- R/whocc.R | 4 +- data-raw/eucast_rules.tsv | 24 +- data-raw/internals.R | 2 +- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/index.html | 2 +- docs/authors.html | 2 +- docs/index.html | 2 +- docs/news/index.html | 21 +- docs/reference/AMR-deprecated.html | 6 +- docs/reference/AMR.html | 8 +- docs/reference/WHOCC.html | 4 +- docs/reference/WHONET.html | 61 +++-- docs/reference/ab_property.html | 24 +- docs/reference/age.html | 10 +- docs/reference/age_groups.html | 8 +- docs/reference/antibiotics.html | 66 ++--- docs/reference/as.ab.html | 14 +- docs/reference/as.disk.html | 6 +- docs/reference/as.mic.html | 10 +- docs/reference/as.mo.html | 93 ++++--- docs/reference/as.rsi.html | 30 +-- docs/reference/atc_online.html | 18 +- docs/reference/availability.html | 12 +- docs/reference/bug_drug_combinations.html | 37 +-- docs/reference/catalogue_of_life.html | 10 +- docs/reference/catalogue_of_life_version.html | 8 +- docs/reference/count.html | 60 +++-- docs/reference/eucast_rules.html | 194 ++++++------- docs/reference/example_isolates.html | 27 +- docs/reference/filter_ab_class.html | 6 +- docs/reference/first_isolate.html | 85 ++++-- docs/reference/g.test.html | 50 ++-- docs/reference/ggplot_rsi.html | 102 ++++--- docs/reference/guess_ab_col.html | 10 +- docs/reference/index.html | 10 +- docs/reference/join.html | 18 +- docs/reference/key_antibiotics.html | 109 +++++--- docs/reference/kurtosis.html | 6 +- docs/reference/like.html | 12 +- docs/reference/mdro.html | 217 ++++++++------- docs/reference/microorganisms.codes.html | 19 +- docs/reference/microorganisms.html | 39 ++- docs/reference/microorganisms.old.html | 27 +- docs/reference/mo_property.html | 43 +-- docs/reference/mo_source.html | 54 ++-- docs/reference/p_symbol.html | 12 +- docs/reference/proportion.html | 90 ++++--- docs/reference/read.4D.html | 29 +- docs/reference/resistance_predict.html | 86 ++++-- docs/reference/rsi_translation.html | 29 +- docs/reference/skewness.html | 6 +- docs/reference/translate.html | 10 +- man/AMR-deprecated.Rd | 3 + man/AMR.Rd | 26 +- man/WHOCC.Rd | 4 +- man/WHONET.Rd | 56 ++-- man/ab_property.Rd | 20 +- man/age.Rd | 8 +- man/age_groups.Rd | 22 +- man/antibiotics.Rd | 62 ++--- man/as.ab.Rd | 15 +- man/as.disk.Rd | 8 +- man/as.mic.Rd | 8 +- man/as.mo.Rd | 118 ++++---- man/as.rsi.Rd | 32 ++- man/atc_online.Rd | 50 ++-- man/availability.Rd | 8 +- man/bug_drug_combinations.Rd | 33 ++- man/catalogue_of_life.Rd | 20 +- man/catalogue_of_life_version.Rd | 8 +- man/count.Rd | 60 ++--- man/eucast_rules.Rd | 222 +++++++-------- man/example_isolates.Rd | 22 +- man/filter_ab_class.Rd | 4 +- man/first_isolate.Rd | 85 +++--- man/g.test.Rd | 51 ++-- man/ggplot_rsi.Rd | 99 ++++--- man/guess_ab_col.Rd | 8 +- man/join.Rd | 10 +- man/key_antibiotics.Rd | 107 +++++--- man/kurtosis.Rd | 4 +- man/like.Rd | 14 +- man/mdro.Rd | 225 ++++++++-------- man/microorganisms.Rd | 44 +-- man/microorganisms.codes.Rd | 12 +- man/microorganisms.old.Rd | 20 +- man/mo_property.Rd | 51 ++-- man/mo_source.Rd | 57 ++-- man/p_symbol.Rd | 4 +- man/proportion.Rd | 96 ++++--- man/read.4D.Rd | 25 +- man/resistance_predict.Rd | 106 +++++--- man/rsi_translation.Rd | 22 +- man/skewness.Rd | 4 +- man/translate.Rd | 6 +- tests/testthat/test-mo_history.R | 24 -- 138 files changed, 2797 insertions(+), 2484 deletions(-) diff --git a/.lintr b/.lintr index 7f972d62c..1e82191e3 100644 --- a/.lintr +++ b/.lintr @@ -1,2 +1 @@ linters: with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_usage_linter = NULL, object_length_linter(length = 50L)) -exclusions: list("R/mo_history.R", "tests/testthat/test-mo_history.R") diff --git a/DESCRIPTION b/DESCRIPTION index fca59ae36..0ac09bccf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: AMR -Version: 0.8.0.9035 -Date: 2019-11-24 +Version: 0.8.0.9036 +Date: 2019-11-28 Title: Antimicrobial Resistance Analysis Authors@R: c( person(role = c("aut", "cre"), @@ -63,4 +63,5 @@ BugReports: https://gitlab.com/msberends/AMR/issues License: GPL-2 | file LICENSE Encoding: UTF-8 LazyData: true -RoxygenNote: 6.1.1 +RoxygenNote: 7.0.1 +Roxygen: list(markdown = TRUE) diff --git a/NAMESPACE b/NAMESPACE index e44adf47d..fe8e58f55 100755 --- a/NAMESPACE +++ b/NAMESPACE @@ -173,6 +173,7 @@ export(portion_IR) export(portion_R) export(portion_S) export(portion_SI) +export(portion_df) export(proportion_I) export(proportion_IR) export(proportion_R) diff --git a/NEWS.md b/NEWS.md index 601516bcc..1ee4f377b 100755 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,5 @@ -# AMR 0.8.0.9035 -## Last updated: 24-Nov-2019 +# AMR 0.8.0.9036 +## Last updated: 28-Nov-2019 ### Breaking * Adopted Adeolu *et al.* (2016), [PMID 27620848](https://www.ncbi.nlm.nih.gov/pubmed/27620848) for the `microorganisms` data set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like *Morganellaceae* and *Yersiniaceae*). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations with `mdro()` will now use the Enterobacterales order for all guidelines before 2016 that were dependent on the Enterobacteriaceae family. @@ -58,8 +58,10 @@ * More robust way of determining valid MIC values * Small changed to the `example_isolates` data set to better reflect reality * Added more microorganisms codes from laboratory systems (esp. species of *Pseudescherichia* and *Rodentibacter*) +* Added Gram-stain to `mo_info()` ### Other +* Rewrote the complete documentation to markdown format, to be able to use the very latest version of the great [Roxygen2](https://roxygen2.r-lib.org/index.html), released in November 2019. This tremously improved the documentation quality, since the rewrite forced us to go over all texts again and make changes where needed. * Change dependency on `clean` to `cleaner`, as this package was renamed accordingly upon CRAN request * Added Dr. Sofia Ny as contributor diff --git a/R/ab.R b/R/ab.R index d898a378c..b4da2c925 100755 --- a/R/ab.R +++ b/R/ab.R @@ -21,25 +21,26 @@ #' Transform to antibiotic ID #' -#' Use this function to determine the antibiotic code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and synonyms (brand names). +#' Use this function to determine the antibiotic code of one or more antibiotics. The data set [antibiotics] will be searched for abbreviations, official names and synonyms (brand names). #' @param x character vector to determine to antibiotic ID #' @param ... arguments passed on to internal functions #' @rdname as.ab #' @inheritSection WHOCC WHOCC -#' @export #' @importFrom dplyr %>% filter slice pull -#' @details All entries in the \code{\link{antibiotics}} data set have three different identifiers: a human readable EARS-Net code (column \code{ab}, used by ECDC and WHONET), an ATC code (column \code{atc}, used by WHO), and a CID code (column \code{cid}, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. +#' @details All entries in the [antibiotics] data set have three different identifiers: a human readable EARS-Net code (column `ab`, used by ECDC and WHONET), an ATC code (column `atc`, used by WHO), and a CID code (column `cid`, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. #' -#' Use the \code{\link{ab_property}} functions to get properties based on the returned antibiotic ID, see Examples. +#' Use the [ab_property()] functions to get properties based on the returned antibiotic ID, see Examples. #' @section Source: #' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/} #' #' WHONET 2019 software: \url{http://www.whonet.org/software.html} #' #' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm} -#' @return Character (vector) with class \code{"ab"}. Unknown values will return \code{NA}. -#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATCs. +#' @aliases ab +#' @return Character (vector) with class [`ab`]. Unknown values will return `NA`. +#' @seealso [antibiotics] for the dataframe that is being used to determine ATCs. #' @inheritSection AMR Read more on our website! +#' @export #' @examples #' # These examples all return "ERY", the ID of Erythromycin: #' as.ab("J01FA01") diff --git a/R/ab_property.R b/R/ab_property.R index 5f6cf6627..ad08b17d5 100644 --- a/R/ab_property.R +++ b/R/ab_property.R @@ -21,26 +21,25 @@ #' Property of an antibiotic #' -#' Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set. All input values will be evaluated internally with \code{\link{as.ab}}. -#' @param x any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.ab}} +#' Use these functions to return a specific property of an antibiotic from the [antibiotics] data set. All input values will be evaluated internally with [as.ab()]. +#' @param x any (vector of) text that can be coerced to a valid microorganism code with [as.ab()] #' @param tolower logical to indicate whether the first character of every output should be transformed to a lower case character. This will lead to e.g. "polymyxin B" and not "polymyxin b". -#' @param property one of the column names of one of the \code{\link{antibiotics}} data set -#' @param language language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation. -#' @param administration way of administration, either \code{"oral"} or \code{"iv"} +#' @param property one of the column names of one of the [antibiotics] data set +#' @param language language of the returned text, defaults to system language (see [get_locale()]) and can also be set with `getOption("AMR_locale")`. Use `language = NULL` or `language = ""` to prevent translation. +#' @param administration way of administration, either `"oral"` or `"iv"` #' @param units a logical to indicate whether the units instead of the DDDs itself must be returned, see Examples -#' @param ... other parameters passed on to \code{\link{as.ab}} -#' @details All output will be \link{translate}d where possible. +#' @param ... other parameters passed on to [as.ab()] +#' @details All output will be [translate]d where possible. #' @inheritSection as.ab Source #' @rdname ab_property #' @name ab_property -#' @return \itemize{ -#' \item{An \code{integer} in case of \code{ab_cid}} -#' \item{A named \code{list} in case of \code{ab_info} and multiple \code{ab_synonyms}/\code{ab_tradenames}} -#' \item{A \code{double} in case of \code{ab_ddd}} -#' \item{A \code{character} in all other cases} -#' } +#' @return +#' - An [`integer`] in case of [ab_cid()] +#' - A named [`list`] in case of [ab_info()] and multiple [ab_synonyms()]/[ab_tradenames()] +#' - A [`double`] in case of [ab_ddd()] +#' - A [`character`] in all other cases #' @export -#' @seealso \code{\link{antibiotics}} +#' @seealso [antibiotics] #' @inheritSection AMR Read more on our website! #' @examples #' # all properties: diff --git a/R/age.R b/R/age.R index c6b301bdc..da8619644 100755 --- a/R/age.R +++ b/R/age.R @@ -22,12 +22,12 @@ #' Age in years of individuals #' #' Calculates age in years based on a reference date, which is the sytem date at default. -#' @param x date(s), will be coerced with \code{\link{as.POSIXlt}} -#' @param reference reference date(s) (defaults to today), will be coerced with \code{\link{as.POSIXlt}} and cannot be lower than \code{x} -#' @param exact a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of \href{https://en.wikipedia.org/wiki/Year-to-date}{year-to-date} (YTD) of \code{x} by the number of days in a year of \code{reference} (either 365 or 366). +#' @param x date(s), will be coerced with [as.POSIXlt()] +#' @param reference reference date(s) (defaults to today), will be coerced with [as.POSIXlt()] and cannot be lower than `x` +#' @param exact a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of [year-to-date](https://en.wikipedia.org/wiki/Year-to-date) (YTD) of `x` by the number of days in the year of `reference` (either 365 or 366). #' @param na.rm a logical to indicate whether missing values should be removed -#' @return An integer (no decimals) if \code{exact = FALSE}, a double (with decimals) otherwise -#' @seealso To split ages into groups, use the \code{\link{age_groups}} function. +#' @return An integer (no decimals) if `exact = FALSE`, a double (with decimals) otherwise +#' @seealso To split ages into groups, use the [age_groups()] function. #' @importFrom dplyr if_else #' @inheritSection AMR Read more on our website! #' @export @@ -90,24 +90,21 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) { #' Split ages into age groups #' -#' Split ages into age groups defined by the \code{split} parameter. This allows for easier demographic (antimicrobial resistance) analysis. -#' @param x age, e.g. calculated with \code{\link{age}} -#' @param split_at values to split \code{x} at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details. +#' Split ages into age groups defined by the `split` parameter. This allows for easier demographic (antimicrobial resistance) analysis. +#' @param x age, e.g. calculated with [age()] +#' @param split_at values to split `x` at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details. #' @param na.rm a logical to indicate whether missing values should be removed #' @details To split ages, the input can be: -#' \itemize{ -#' \item{A numeric vector. A vector of e.g. \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+. -#' The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).} -#' \item{A character:} -#' \itemize{ -#' \item{\code{"children"} or \code{"kids"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.} -#' \item{\code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85)}. This will split on 0-64, 65-74, 75-84, 85+.} -#' \item{\code{"fives"}, equivalent of: \code{1:20 * 5}. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.} -#' \item{\code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.} -#' } -#' } -#' @return Ordered \code{\link{factor}} -#' @seealso To determine ages, based on one or more reference dates, use the \code{\link{age}} function. +#' +#' * A numeric vector. A vector of e.g. `c(10, 20)` will split on 0-9, 10-19 and 20+. A value of only `50` will split on 0-49 and 50+. +#' The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+). +#' * A character: +#' - `"children"` or `"kids"`, equivalent of: `c(0, 1, 2, 4, 6, 13, 18)`. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+. +#' - `"elderly"` or `"seniors"`, equivalent of: `c(65, 75, 85)`. This will split on 0-64, 65-74, 75-84, 85+. +#' - `"fives"`, equivalent of: `1:20 * 5`. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+. +#' - `"tens"`, equivalent of: `1:10 * 10`. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+. +#' @return Ordered [`factor`] +#' @seealso To determine ages, based on one or more reference dates, use the [age()] function. #' @export #' @inheritSection AMR Read more on our website! #' @examples diff --git a/R/amr.R b/R/amr.R index c07dbdc40..e061ae502 100644 --- a/R/amr.R +++ b/R/amr.R @@ -19,30 +19,29 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' The \code{AMR} Package +#' The `AMR` Package #' -#' Welcome to the \code{AMR} package. +#' Welcome to the `AMR` package. #' @details -#' \code{AMR} is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data. +#' `AMR` is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data. #' #' We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). 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 can be used for: -#' \itemize{ -#' \item{Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life} -#' \item{Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines} -#' \item{Determining first isolates to be used for AMR analysis} -#' \item{Calculating antimicrobial resistance} -#' \item{Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)} -#' \item{Calculating (empirical) susceptibility of both mono therapy and combination therapies} -#' \item{Predicting future antimicrobial resistance using regression models} -#' \item{Getting properties for any microorganism (like Gram stain, species, genus or family)} -#' \item{Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)} -#' \item{Plotting antimicrobial resistance} -#' \item{Applying EUCAST expert rules} -#' } +#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the [Catalogue of Life](http://www.catalogueoflife.org) +#' - Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines +#' - Determining first isolates to be used for AMR analysis +#' - Calculating antimicrobial resistance +#' - Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) +#' - Calculating (empirical) susceptibility of both mono therapy and combination therapies +#' - Predicting future antimicrobial resistance using regression models +#' - Getting properties for any microorganism (like Gram stain, species, genus or family) +#' - Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name) +#' - Plotting antimicrobial resistance +#' - Applying EUCAST expert rules + #' @section Read more on our website!: -#' On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}. +#' On our website you can find [a tutorial](https://msberends.gitlab.io/AMR/articles/AMR.html) about how to conduct AMR analysis, the [complete documentation of all functions](https://msberends.gitlab.io/AMR/reference) (which reads a lot easier than here in R) and [an example analysis using WHONET data](https://msberends.gitlab.io/AMR/articles/WHONET.html). #' @section Contact us: #' For suggestions, comments or questions, please contact us at: #' @@ -51,11 +50,11 @@ #' Department of Medical Microbiology, University of Groningen \cr #' University Medical Center Groningen \cr #' Post Office Box 30001 \cr -#' 9700 RB Groningen +#' 9700 RB Groningen \cr #' The Netherlands #' #' If you have found a bug, please file a new issue at: \cr -#' \url{https://gitlab.com/msberends/AMR/issues} +#' #' @name AMR #' @rdname AMR #' @importFrom microbenchmark microbenchmark diff --git a/R/atc_online.R b/R/atc_online.R index 04ca0a2c2..598a875aa 100644 --- a/R/atc_online.R +++ b/R/atc_online.R @@ -21,43 +21,43 @@ #' Get ATC properties from WHOCC website #' -#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \cr \strong{This function requires an internet connection.} +#' @description Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. +#' +#' **This function requires an internet connection.** #' @param atc_code a character or character vector with ATC code(s) of antibiotic(s) -#' @param property property of an ATC code. Valid values are \code{"ATC"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"}, \code{"Note"} and \code{groups}. For this last option, all hierarchical groups of an ATC code will be returned, see Examples. -#' @param administration type of administration when using \code{property = "Adm.R"}, see Details -#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes. -#' @param ... parameters to pass on to \code{atc_property} +#' @param property property of an ATC code. Valid values are `"ATC"`, `"Name"`, `"DDD"`, `"U"` (`"unit"`), `"Adm.R"`, `"Note"` and `groups`. For this last option, all hierarchical groups of an ATC code will be returned, see Examples. +#' @param administration type of administration when using `property = "Adm.R"`, see Details +#' @param url url of website of the WHO. The sign `%s` can be used as a placeholder for ATC codes. +#' @param ... parameters to pass on to `atc_property` #' @details -#' Options for parameter \code{administration}: -#' \itemize{ -#' \item{\code{"Implant"}}{ = Implant} -#' \item{\code{"Inhal"}}{ = Inhalation} -#' \item{\code{"Instill"}}{ = Instillation} -#' \item{\code{"N"}}{ = nasal} -#' \item{\code{"O"}}{ = oral} -#' \item{\code{"P"}}{ = parenteral} -#' \item{\code{"R"}}{ = rectal} -#' \item{\code{"SL"}}{ = sublingual/buccal} -#' \item{\code{"TD"}}{ = transdermal} -#' \item{\code{"V"}}{ = vaginal} -#' } +#' Options for parameter `administration`: +#' +#' - `"Implant"` = Implant +#' - `"Inhal"` = Inhalation +#' - `"Instill"` = Instillation +#' - `"N"` = nasal +#' - `"O"` = oral +#' - `"P"` = parenteral +#' - `"R"` = rectal +#' - `"SL"` = sublingual/buccal +#' - `"TD"` = transdermal +#' - `"V"` = vaginal #' -#' Abbreviations of return values when using \code{property = "U"} (unit): -#' \itemize{ -#' \item{\code{"g"}}{ = gram} -#' \item{\code{"mg"}}{ = milligram} -#' \item{\code{"mcg"}}{ = microgram} -#' \item{\code{"U"}}{ = unit} -#' \item{\code{"TU"}}{ = thousand units} -#' \item{\code{"MU"}}{ = million units} -#' \item{\code{"mmol"}}{ = millimole} -#' \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)} -#' } +#' Abbreviations of return values when using `property = "U"` (unit): +#' +#' - `"g"` = gram +#' - `"mg"` = milligram +#' - `"mcg"`` = microgram +#' - `"U"` = unit +#' - `"TU"` = thousand units +#' - `"MU"` = million units +#' - `"mmol"` = millimole +#' - `"ml"` = milliliter (e.g. eyedrops) #' @export #' @rdname atc_online #' @importFrom dplyr %>% progress_estimated #' @inheritSection AMR Read more on our website! -#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/} +#' @source #' @examples #' \donttest{ #' # oral DDD (Defined Daily Dose) of amoxicillin diff --git a/R/availability.R b/R/availability.R index 5fe4c018e..4fbfa4f44 100644 --- a/R/availability.R +++ b/R/availability.R @@ -21,11 +21,11 @@ #' Check availability of columns #' -#' Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. \code{\link{resistance}}. -#' @param tbl a \code{data.frame} or \code{list} +#' Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. [resistance()]. +#' @param tbl a [`data.frame`] or [`list`] #' @param width number of characters to present the visual availability, defaults to filling the width of the console -#' @details The function returns a \code{data.frame} with columns \code{"resistant"} and \code{"visual_resistance"}. The values in that columns are calculated with \code{\link{resistance}}. -#' @return \code{data.frame} with column names of \code{tbl} as row names +#' @details The function returns a [`data.frame`] with columns `"resistant"` and `"visual_resistance"`. The values in that columns are calculated with [resistance()]. +#' @return [`data.frame`] with column names of `tbl` as row names #' @inheritSection AMR Read more on our website! #' @importFrom cleaner percentage #' @export diff --git a/R/bug_drug_combinations.R b/R/bug_drug_combinations.R index 41c19b18b..50d408b81 100644 --- a/R/bug_drug_combinations.R +++ b/R/bug_drug_combinations.R @@ -21,25 +21,25 @@ #' Determine bug-drug combinations #' -#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{format} on the result to prettify it to a publicable/printable format, see Examples. +#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use [format()] on the result to prettify it to a publicable/printable format, see Examples. #' @inheritParams eucast_rules #' @param combine_IR logical to indicate whether values R and I should be summed #' @param add_ab_group logical to indicate where the group of the antimicrobials must be included as a first column -#' @param remove_intrinsic_resistant logical to indicate that rows with 100\% resistance for all tested antimicrobials must be removed from the table -#' @param FUN the function to call on the \code{mo} column to transform the microorganism IDs, defaults to \code{\link{mo_shortname}} -#' @param translate_ab a character of length 1 containing column names of the \code{\link{antibiotics}} data set -#' @param ... arguments passed on to \code{FUN} +#' @param remove_intrinsic_resistant logical to indicate that rows with 100% resistance for all tested antimicrobials must be removed from the table +#' @param FUN the function to call on the `mo` column to transform the microorganism IDs, defaults to [mo_shortname()] +#' @param translate_ab a character of length 1 containing column names of the [antibiotics] data set +#' @param ... arguments passed on to `FUN` #' @inheritParams rsi_df #' @inheritParams base::formatC #' @importFrom dplyr %>% rename group_by select mutate filter summarise ungroup #' @importFrom tidyr pivot_longer -#' @details The function \code{format} calculates the resistance per bug-drug combination. Use \code{combine_IR = FALSE} (default) to test R vs. S+I and \code{combine_IR = TRUE} to test R+I vs. S. +#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_IR = FALSE` (default) to test R vs. S+I and `combine_IR = TRUE` to test R+I vs. S. #' -#' The language of the output can be overwritten with \code{options(AMR_locale)}, please see \link{translate}. +#' The language of the output can be overwritten with `options(AMR_locale)`, please see [translate]. #' @export #' @rdname bug_drug_combinations -#' @return The function \code{bug_drug_combinations} returns a \code{data.frame} with columns "mo", "ab", "S", "I", "R" and "total". -#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. +#' @return The function [bug_drug_combinations()] returns a [`data.frame`] with columns "mo", "ab", "S", "I", "R" and "total". +#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. . #' @inheritSection AMR Read more on our website! #' @examples #' \donttest{ diff --git a/R/catalogue_of_life.R b/R/catalogue_of_life.R index edb11c692..3e8fa9f89 100755 --- a/R/catalogue_of_life.R +++ b/R/catalogue_of_life.R @@ -24,28 +24,26 @@ #' This package contains the complete taxonomic tree of almost all microorganisms from the authoritative and comprehensive Catalogue of Life. #' @section Catalogue of Life: #' \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr} -#' This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available. +#' This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available. #' -#' \link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}. +#' [Click here][catalogue_of_life] for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with [catalogue_of_life_version()]. #' @section Included taxa: #' Included are: -#' \itemize{ -#' \item{All ~61,000 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa} -#' \item{All ~8,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (like all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).} -#' \item{All ~150 (sub)species from ~100 other relevant genera from the kingdom of Animalia (like \emph{Strongyloides} and \emph{Taenia})} -#' \item{All ~23,000 previously accepted names of all included (sub)species (these were taxonomically renamed)} -#' \item{The complete taxonomic tree of all included (sub)species: from kingdom to subspecies} -#' \item{The responsible author(s) and year of scientific publication} -#' } +#' - All ~61,000 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa +#' - All ~8,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (like all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*). +#' - All ~150 (sub)species from ~100 other relevant genera from the kingdom of Animalia (like *Strongyloides* and *Taenia*) +#' - All ~23,000 previously accepted names of all included (sub)species (these were taxonomically renamed) +#' - The complete taxonomic tree of all included (sub)species: from kingdom to subspecies +#' - The responsible author(s) and year of scientific publication #' -#' The Catalogue of Life (\url{http://www.catalogueoflife.org}) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation. +#' The Catalogue of Life () is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation. #' -#' The syntax used to transform the original data to a cleansed R format, can be found here: \url{https://gitlab.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R}. +#' The syntax used to transform the original data to a cleansed R format, can be found here: . #' @inheritSection AMR Read more on our website! #' @name catalogue_of_life #' @rdname catalogue_of_life -#' @seealso Data set \code{\link{microorganisms}} for the actual data. \cr -#' Function \code{\link{as.mo}()} to use the data for intelligent determination of microorganisms. +#' @seealso Data set [microorganisms] for the actual data. \cr +#' Function [as.mo()] to use the data for intelligent determination of microorganisms. #' @examples #' # Get version info of included data set #' catalogue_of_life_version() @@ -70,7 +68,7 @@ #' mo_ref("E. coli") #' # [1] "Castellani et al., 1919" #' -#' # Do not get mistaken - the package only includes microorganisms +#' # Do not get mistaken - this package is about microorganisms #' mo_kingdom("C. elegans") #' # [1] "Bacteria" # Bacteria?! #' mo_name("C. elegans") @@ -80,9 +78,9 @@ NULL #' Version info of included Catalogue of Life #' #' This function returns information about the included data from the Catalogue of Life. -#' @seealso \code{\link{microorganisms}} -#' @details For DSMZ, see \code{?microorganisms}. -#' @return a \code{list}, which prints in pretty format +#' @seealso [microorganisms] +#' @details For DSMZ, see [microorganisms]. +#' @return a [`list`], which prints in pretty format #' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection AMR Read more on our website! #' @importFrom crayon bold underline diff --git a/R/count.R b/R/count.R index 6ff25554b..c2ac1b71d 100755 --- a/R/count.R +++ b/R/count.R @@ -19,24 +19,24 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' Count isolates +#' Count available isolates #' -#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. +#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in [summarise()] and support grouped variables, see *Examples*. #' -#' \code{count_resistant()} should be used to count resistant isolates, \code{count_susceptible()} should be used to count susceptible isolates.\cr -#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. +#' [count_resistant()] should be used to count resistant isolates, [count_susceptible()] should be used to count susceptible isolates. +#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [as.rsi()] if needed. #' @inheritParams proportion #' @inheritSection as.rsi Interpretation of S, I and R -#' @details These functions are meant to count isolates. Use the \code{\link{resistance}}/\code{\link{susceptibility}} functions to calculate microbial resistance/susceptibility. +#' @details These functions are meant to count isolates. Use the [resistance()]/[susceptibility()] functions to calculate microbial resistance/susceptibility. #' -#' The function \code{count_resistant()} is equal to the function \code{count_R()}. The function \code{count_susceptible()} is equal to the function \code{count_SI()}. +#' The function [count_resistant()] is equal to the function [count_R()]. The function [count_susceptible()] is equal to the function [count_SI()]. #' -#' The function \code{n_rsi()} is an alias of \code{count_all()}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}()}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}. +#' The function [n_rsi()] is an alias of [count_all()]. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to [n_distinct()]. Their function is equal to `count_susceptible(...) + count_resistant(...)`. #' -#' The function \code{count_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and counts the number of S's, I's and R's. The function \code{rsi_df()} works exactly like \code{count_df()}, but adds the percentage of S, I and R. +#' The function [count_df()] takes any variable from `data` that has an [`rsi`] class (created with [as.rsi()]) and counts the number of S's, I's and R's. The function [rsi_df()] works exactly like [count_df()], but adds the percentage of S, I and R. #' @inheritSection proportion Combination therapy -#' @seealso \code{\link{proportion}_*} to calculate microbial resistance and susceptibility. -#' @return Integer +#' @seealso [`proportion_*`][proportion] to calculate microbial resistance and susceptibility. +#' @return An [`integer`] #' @rdname count #' @name count #' @export diff --git a/R/data.R b/R/data.R index 859c32d61..fd69b7cbd 100755 --- a/R/data.R +++ b/R/data.R @@ -21,48 +21,44 @@ #' Data sets with ~550 antimicrobials #' -#' Two data sets containing all antibiotics/antimycotics and antivirals. Use \code{\link{as.ab}} or one of the \code{\link{ab_property}} functions to retrieve values from the \code{antibiotics} data set. Three identifiers are included in this data set: an antibiotic ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes. +#' Two data sets containing all antibiotics/antimycotics and antivirals. Use [as.ab()] or one of the [ab_property()] functions to retrieve values from the [antibiotics] data set. Three identifiers are included in this data set: an antibiotic ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. #' @format -#' \strong{For the \code{antibiotics} data set: a \code{\link{data.frame}} with 452 observations and 13 variables:} -#' \describe{ -#' \item{\code{ab}}{Antibiotic ID as used in this package (like \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available} -#' \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02}} -#' \item{\code{cid}}{Compound ID as found in PubChem} -#' \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO} -#' \item{\code{group}}{A short and concise group name, based on WHONET and WHOCC definitions} -#' \item{\code{atc_group1}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like \code{"Macrolides, lincosamides and streptogramins"}} -#' \item{\code{atc_group2}}{Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like \code{"Macrolides"}} -#' \item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)} -#' \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID} -#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment} -#' \item{\code{oral_units}}{Units of \code{oral_ddd}} -#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment} -#' \item{\code{iv_units}}{Units of \code{iv_ddd}} -#' } +#' ### For the [antibiotics] data set: a [`data.frame`] with 452 observations and 13 variables: +#' - `ab`\cr Antibiotic ID as used in this package (like `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available +#' - `atc`\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like `J01CR02` +#' - `cid`\cr Compound ID as found in PubChem +#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO +#' - `group`\cr A short and concise group name, based on WHONET and WHOCC definitions +#' - `atc_group1`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like `"Macrolides, lincosamides and streptogramins"` +#' - `atc_group2`\cr Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like `"Macrolides"` +#' - `abbr`\cr List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST) +#' - `synonyms`\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID +#' - `oral_ddd`\cr Defined Daily Dose (DDD), oral treatment +#' - `oral_units`\cr Units of `oral_ddd` +#' - `iv_ddd`\cr Defined Daily Dose (DDD), parenteral treatment +#' - `iv_units`\cr Units of `iv_ddd` #' -#' \strong{For the \code{antivirals} data set: a \code{\link{data.frame}} with 102 observations and 9 variables:} -#' \describe{ -#' \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC} -#' \item{\code{cid}}{Compound ID as found in PubChem} -#' \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO} -#' \item{\code{atc_group}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC} -#' \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID} -#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment} -#' \item{\code{oral_units}}{Units of \code{oral_ddd}} -#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment} -#' \item{\code{iv_units}}{Units of \code{iv_ddd}} -#' } -#' @details Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}. +#' ### For the [antivirals] data set: a [`data.frame`] with 102 observations and 9 variables: +#' - `atc`\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC +#' - `cid`\cr Compound ID as found in PubChem +#' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO +#' - `atc_group`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC +#' - `synonyms`\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID +#' - `oral_ddd`\cr Defined Daily Dose (DDD), oral treatment +#' - `oral_units`\cr Units of `oral_ddd` +#' - `iv_ddd`\cr Defined Daily Dose (DDD), parenteral treatment +#' - `iv_units`\cr Units of `iv_ddd` +#' @details Properties that are based on an ATC code are only available when an ATC is available. These properties are: `atc_group1`, `atc_group2`, `oral_ddd`, `oral_units`, `iv_ddd` and `iv_units`. #' -#' Synonyms (i.e. trade names) are derived from the Compound ID (\code{cid}) and consequently only available where a CID is available. -#' @source World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/} +#' Synonyms (i.e. trade names) are derived from the Compound ID (`cid`) and consequently only available where a CID is available. +#' @source World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): #' -#' WHONET 2019 software: \url{http://www.whonet.org/software.html} +#' WHONET 2019 software: #' -#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{http://ec.europa.eu/health/documents/community-register/html/atc.htm} +#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: #' @inheritSection WHOCC WHOCC #' @inheritSection AMR Read more on our website! -#' @seealso \code{\link{microorganisms}} +#' @seealso [microorganisms] "antibiotics" #' @rdname antibiotics @@ -70,39 +66,35 @@ #' Data set with ~70,000 microorganisms #' -#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}. +#' A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using [as.mo()]. #' @inheritSection catalogue_of_life Catalogue of Life -#' @format A \code{\link{data.frame}} with 69,447 observations and 16 variables: -#' \describe{ -#' \item{\code{mo}}{ID of microorganism as used by this package} -#' \item{\code{col_id}}{Catalogue of Life ID} -#' \item{\code{fullname}}{Full name, like \code{"Escherichia coli"}} -#' \item{\code{kingdom}, \code{phylum}, \code{class}, \code{order}, \code{family}, \code{genus}, \code{species}, \code{subspecies}}{Taxonomic rank of the microorganism} -#' \item{\code{rank}}{Text of the taxonomic rank of the microorganism, like \code{"species"} or \code{"genus"}} -#' \item{\code{ref}}{Author(s) and year of concerning scientific publication} -#' \item{\code{species_id}}{ID of the species as used by the Catalogue of Life} -#' \item{\code{source}}{Either "CoL", "DSMZ" (see Source) or "manually added"} -#' \item{\code{prevalence}}{Prevalence of the microorganism, see \code{?as.mo}} -#' } +#' @format A [`data.frame`] with 69,447 observations and 16 variables: +#' - `mo`\cr ID of microorganism as used by this package +#' - `col_id`\cr Catalogue of Life ID +#' - `fullname`\cr Full name, like `"Escherichia coli"` +#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism +#' - `rank`\cr Text of the taxonomic rank of the microorganism, like `"species"` or `"genus"` +#' - `ref`\cr Author(s) and year of concerning scientific publication +#' - `species_id`\cr ID of the species as used by the Catalogue of Life +#' - `source`\cr Either "CoL", "DSMZ" (see Source) or "manually added" +#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()] #' @details Manually added were: -#' \itemize{ -#' \item{11 entries of \emph{Streptococcus} (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)} -#' \item{2 entries of \emph{Staphylococcus} (coagulase-negative [CoNS] and coagulase-positive [CoPS])} -#' \item{3 entries of \emph{Trichomonas} (\emph{Trichomonas vaginalis}, and its family and genus)} -#' \item{1 entry of \emph{Blastocystis} (\emph{Blastocystis hominis}), although it officially does not exist (Noel \emph{et al.} 2005, PMID 15634993)} -#' \item{5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)} -#' \item{6 families under the Enterobacterales order, according to Adeolu \emph{et al.} (2016, PMID 27620848), that are not in the Catalogue of Life} -#' \item{12,600 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications} -#' } +#' - 11 entries of *Streptococcus* (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri) +#' - 2 entries of *Staphylococcus* (coagulase-negative [CoNS] and coagulase-positive [CoPS]) +#' - 3 entries of *Trichomonas* (*Trichomonas vaginalis*, and its family and genus) +#' - 1 entry of *Blastocystis* (*Blastocystis hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993) +#' - 5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus) +#' - 6 families under the Enterobacterales order, according to Adeolu *et al.* (2016, PMID 27620848), that are not in the Catalogue of Life +#' - 12,600 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications #' @section About the records from DSMZ (see source): #' Names of prokaryotes are defined as being validly published by the International Code of Nomenclature of Bacteria. Validly published are all names which are included in the Approved Lists of Bacterial Names and the names subsequently published in the International Journal of Systematic Bacteriology (IJSB) and, from January 2000, in the International Journal of Systematic and Evolutionary Microbiology (IJSEM) as original articles or in the validation lists. #' -#' From: \url{https://www.dsmz.de/support/bacterial-nomenclature-up-to-date-downloads/readme.html} -#' @source 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}()}). +#' From: +#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), (check included annual version with [catalogue_of_life_version()]). #' -#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, \url{http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date} (check included version with \code{\link{catalogue_of_life_version}()}). +#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, (check included version with [catalogue_of_life_version()]). #' @inheritSection AMR Read more on our website! -#' @seealso \code{\link{as.mo}}, \code{\link{mo_property}}, \code{\link{microorganisms.codes}} +#' @seealso [as.mo()], [mo_property()], [microorganisms.codes] "microorganisms" catalogue_of_life <- list( @@ -115,103 +107,93 @@ catalogue_of_life <- list( #' 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 \code{\link{as.mo}}. +#' 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()]. #' @inheritSection catalogue_of_life Catalogue of Life -#' @format A \code{\link{data.frame}} with 24,246 observations and 5 variables: -#' \describe{ -#' \item{\code{col_id}}{Catalogue of Life ID that was originally given} -#' \item{\code{col_id_new}}{New Catalogue of Life ID that responds to an entry in the \code{\link{microorganisms}} data set} -#' \item{\code{fullname}}{Old full taxonomic name of the microorganism} -#' \item{\code{ref}}{Author(s) and year of concerning scientific publication} -#' \item{\code{prevalence}}{Prevalence of the microorganism, see \code{?as.mo}} -#' } -#' @source 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}()}). +#' @format A [`data.frame`] with 24,246 observations and 5 variables: +#' - `col_id`\cr Catalogue of Life ID that was originally given +#' - `col_id_new`\cr New Catalogue of Life ID that responds to an entry in the [microorganisms] data set +#' - `fullname`\cr Old full taxonomic name of the microorganism +#' - `ref`\cr Author(s) and year of concerning scientific publication +#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()] +#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), (check included annual version with [catalogue_of_life_version()]). #' @inheritSection AMR Read more on our website! -#' @seealso \code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms}} +#' @seealso [as.mo()] [mo_property()] [microorganisms] "microorganisms.old" #' Translation table for common microorganism codes #' -#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with \code{\link{set_mo_source}}. -#' @format A \code{\link{data.frame}} with 5,433 observations and 2 variables: -#' \describe{ -#' \item{\code{code}}{Commonly used code of a microorganism} -#' \item{\code{mo}}{ID of the microorganism in the \code{\link{microorganisms}} data set} -#' } +#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with [set_mo_source()]. +#' @format A [`data.frame`] with 5,433 observations and 2 variables: +#' - `code`\cr Commonly used code of a microorganism +#' - `mo`\cr ID of the microorganism in the [microorganisms] data set #' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection AMR Read more on our website! -#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}} +#' @seealso [as.mo()] [microorganisms] "microorganisms.codes" #' Data set with 2,000 example isolates #' -#' A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{the tutorial on our website}. -#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables: -#' \describe{ -#' \item{\code{date}}{date of receipt at the laboratory} -#' \item{\code{hospital_id}}{ID of the hospital, from A to D} -#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit} -#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward} -#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic} -#' \item{\code{age}}{age of the patient} -#' \item{\code{gender}}{gender of the patient} -#' \item{\code{patient_id}}{ID of the patient} -#' \item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}} -#' \item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}} -#' } +#' A data set containing 2,000 microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR analysis. For examples, please read [the tutorial on our website](https://msberends.gitlab.io/AMR/articles/AMR.html). +#' @format A [`data.frame`] with 2,000 observations and 49 variables: +#' - `date`\cr date of receipt at the laboratory +#' - `hospital_id`\cr ID of the hospital, from A to D +#' - `ward_icu`\cr logical to determine if ward is an intensive care unit +#' - `ward_clinical`\cr logical to determine if ward is a regular clinical ward +#' - `ward_outpatient`\cr logical to determine if ward is an outpatient clinic +#' - `age`\cr age of the patient +#' - `gender`\cr gender of the patient +#' - `patient_id`\cr ID of the patient +#' - `mo`\cr ID of microorganism created with [as.mo()], see also [microorganisms] +#' - `PEN:RIF`\cr 40 different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in [antibiotics] data set and can be translated with [ab_name()] #' @inheritSection AMR Read more on our website! "example_isolates" #' Data set with 500 isolates - WHONET example #' -#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our \code{\link{example_isolates}} data set. -#' @format A \code{\link{data.frame}} with 500 observations and 53 variables: -#' \describe{ -#' \item{\code{Identification number}}{ID of the sample} -#' \item{\code{Specimen number}}{ID of the specimen} -#' \item{\code{Organism}}{Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using \code{\link{as.mo}}.} -#' \item{\code{Country}}{Country of origin} -#' \item{\code{Laboratory}}{Name of laboratory} -#' \item{\code{Last name}}{Last name of patient} -#' \item{\code{First name}}{Initial of patient} -#' \item{\code{Sex}}{Gender of patient} -#' \item{\code{Age}}{Age of patient} -#' \item{\code{Age category}}{Age group, can also be looked up using \code{\link{age_groups}}} -#' \item{\code{Date of admission}}{Date of hospital admission} -#' \item{\code{Specimen date}}{Date when specimen was received at laboratory} -#' \item{\code{Specimen type}}{Specimen type or group} -#' \item{\code{Specimen type (Numeric)}}{Translation of \code{"Specimen type"}} -#' \item{\code{Reason}}{Reason of request with Differential Diagnosis} -#' \item{\code{Isolate number}}{ID of isolate} -#' \item{\code{Organism type}}{Type of microorganism, can also be looked up using \code{\link{mo_type}}} -#' \item{\code{Serotype}}{Serotype of microorganism} -#' \item{\code{Beta-lactamase}}{Microorganism produces beta-lactamase?} -#' \item{\code{ESBL}}{Microorganism produces extended spectrum beta-lactamase?} -#' \item{\code{Carbapenemase}}{Microorganism produces carbapenemase?} -#' \item{\code{MRSA screening test}}{Microorganism is possible MRSA?} -#' \item{\code{Inducible clindamycin resistance}}{Clindamycin can be induced?} -#' \item{\code{Comment}}{Other comments} -#' \item{\code{Date of data entry}}{Date this data was entered in WHONET} -#' \item{\code{AMP_ND10:CIP_EE}}{27 different antibiotics. You can lookup the abbreviatons in the \code{\link{antibiotics}} data set, or use e.g. \code{\link{ab_name}("AMP")} to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using \code{\link{as.rsi}}.} -#' } +#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our [example_isolates] data set. +#' @format A [`data.frame`] with 500 observations and 53 variables: +#' - `Identification number`\cr ID of the sample +#' - `Specimen number`\cr ID of the specimen +#' - `Organism`\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using [as.mo()]. +#' - `Country`\cr Country of origin +#' - `Laboratory`\cr Name of laboratory +#' - `Last name`\cr Last name of patient +#' - `First name`\cr Initial of patient +#' - `Sex`\cr Gender of patient +#' - `Age`\cr Age of patient +#' - `Age category`\cr Age group, can also be looked up using [age_groups()] +#' - `Date of admission`\cr Date of hospital admission +#' - `Specimen date`\cr Date when specimen was received at laboratory +#' - `Specimen type`\cr Specimen type or group +#' - `Specimen type (Numeric)`\cr Translation of `"Specimen type"` +#' - `Reason`\cr Reason of request with Differential Diagnosis +#' - `Isolate number`\cr ID of isolate +#' - `Organism type`\cr Type of microorganism, can also be looked up using [mo_type()] +#' - `Serotype`\cr Serotype of microorganism +#' - `Beta-lactamase`\cr Microorganism produces beta-lactamase? +#' - `ESBL`\cr Microorganism produces extended spectrum beta-lactamase? +#' - `Carbapenemase`\cr Microorganism produces carbapenemase? +#' - `MRSA screening test`\cr Microorganism is possible MRSA? +#' - `Inducible clindamycin resistance`\cr Clindamycin can be induced? +#' - `Comment`\cr Other comments +#' - `Date of data entry`\cr Date this data was entered in WHONET +#' - `AMP_ND10:CIP_EE`\cr 27 different antibiotics. You can lookup the abbreviatons in the [antibiotics] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using [as.rsi()]. #' @inheritSection AMR Read more on our website! "WHONET" #' Data set for RSI interpretation #' -#' Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link{as.rsi}} to transform MICs or disks measurements to RSI values. -#' @format A \code{\link{data.frame}} with 13,975 observations and 9 variables: -#' \describe{ -#' \item{\code{guideline}}{Name of the guideline} -#' \item{\code{method}}{Either "MIC" or "DISK"} -#' \item{\code{site}}{Body site, e.g. "Oral" or "Respiratory"} -#' \item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}} -#' \item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}} -#' \item{\code{ref_tbl}}{Info about where the guideline rule can be found} -#' \item{\code{disk_dose}}{Dose of the used disk diffusion method} -#' \item{\code{breakpoint_S}}{Lowest MIC value or highest number of millimeters that leads to "S"} -#' \item{\code{breakpoint_R}}{Highest MIC value or lowest number of millimeters that leads to "R"} -#' } +#' Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use [as.rsi()] to transform MICs or disks measurements to RSI values. +#' @format A [`data.frame`] with 13,975 observations and 9 variables: +#' - `guideline`\cr Name of the guideline +#' - `method`\cr Either "MIC" or "DISK" +#' - `site`\cr Body site, e.g. "Oral" or "Respiratory" +#' - `mo`\cr Microbial ID, see [as.mo()] +#' - `ab`\cr Antibiotic ID, see [as.ab()] +#' - `ref_tbl`\cr Info about where the guideline rule can be found +#' - `disk_dose`\cr Dose of the used disk diffusion method +#' - `breakpoint_S`\cr Lowest MIC value or highest number of millimeters that leads to "S" +#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimeters that leads to "R" #' @inheritSection AMR Read more on our website! "rsi_translation" diff --git a/R/deprecated.R b/R/deprecated.R index cd4c625b6..68acdc3dd 100755 --- a/R/deprecated.R +++ b/R/deprecated.R @@ -21,48 +21,55 @@ #' Deprecated functions #' -#' These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one). +#' These functions are so-called '[Deprecated]'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one). #' @inheritSection AMR Read more on our website! #' @export #' @keywords internal #' @name AMR-deprecated #' @rdname AMR-deprecated p.symbol <- function(...) { - .Deprecated("p_symbol", package = "AMR") + .Deprecated("p_symbol()", package = "AMR") AMR::p_symbol(...) } #' @rdname AMR-deprecated #' @export portion_R <- function(...) { - .Deprecated("resistance", package = "AMR") + .Deprecated("resistance()", package = "AMR") proportion_R(...) } #' @rdname AMR-deprecated #' @export portion_IR <- function(...) { - .Deprecated("proportion_IR", package = "AMR") + .Deprecated("proportion_IR()", package = "AMR") proportion_IR(...) } #' @rdname AMR-deprecated #' @export portion_I <- function(...) { - .Deprecated("proportion_I", package = "AMR") + .Deprecated("proportion_I()", package = "AMR") proportion_I(...) } #' @rdname AMR-deprecated #' @export portion_SI <- function(...) { - .Deprecated("susceptibility", package = "AMR") + .Deprecated("susceptibility()", package = "AMR") proportion_SI(...) } #' @rdname AMR-deprecated #' @export portion_S <- function(...) { - .Deprecated("proportion_S", package = "AMR") + .Deprecated("proportion_S()", package = "AMR") proportion_S(...) } + +#' @rdname AMR-deprecated +#' @export +portion_df <- function(...) { + .Deprecated("proportion_df()", package = "AMR") + proportion_df(...) +} diff --git a/R/disk.R b/R/disk.R index 264dfba1d..2104dc442 100644 --- a/R/disk.R +++ b/R/disk.R @@ -21,15 +21,15 @@ #' Class 'disk' #' -#' This transforms a vector to a new class \code{disk}, which is a growth zone size (around an antibiotic disk) in millimeters between 6 and 99. +#' This transforms a vector to a new class [`disk`], which is a growth zone size (around an antibiotic disk) in millimeters between 6 and 99. #' @rdname as.disk #' @param x vector #' @param na.rm a logical indicating whether missing values should be removed -#' @details Interpret disk values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI. -#' @return Ordered integer factor with new class \code{disk} +#' @details Interpret disk values as RSI values with [as.rsi()]. It supports guidelines from EUCAST and CLSI. +#' @return Ordered integer factor with new class [`disk`] #' @aliases disk #' @export -#' @seealso \code{\link{as.rsi}} +#' @seealso [as.rsi()] #' @inheritSection AMR Read more on our website! #' @examples #' # interpret disk values diff --git a/R/eucast_rules.R b/R/eucast_rules.R index 6f10cae5b..f90569949 100755 --- a/R/eucast_rules.R +++ b/R/eucast_rules.R @@ -26,141 +26,131 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016" #' EUCAST rules #' #' @description -#' Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables. +#' Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, ), see *Source*. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables. #' #' To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules are applied at default, see Details. -#' @param x data with antibiotic columns, like e.g. \code{AMX} and \code{AMC} +#' @param x data with antibiotic columns, like e.g. `AMX` and `AMC` #' @param info print progress -#' @param rules a character vector that specifies which rules should be applied - one or more of \code{c("breakpoints", "expert", "other", "all")} +#' @param rules a character vector that specifies which rules should be applied - one or more of `c("breakpoints", "expert", "other", "all")` #' @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. -#' @param ... column name of an antibiotic, see section Antibiotics +#' @param ... column name of an antibiotic, please see section *Antibiotics* below #' @inheritParams first_isolate #' @details -#' \strong{Note:} This function does not translate MIC values to RSI values. Use \code{\link{as.rsi}} for that. \cr -#' \strong{Note:} When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance. +#' **Note:** This function does not translate MIC values to RSI values. Use [as.rsi()] for that. \cr +#' **Note:** When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance. #' #' Before further processing, some non-EUCAST rules are applied to improve the efficacy of the EUCAST rules. These non-EUCAST rules, that are applied to all isolates, are: -#' \itemize{ -#' \item{Inherit amoxicillin (AMX) from ampicillin (AMP), where amoxicillin (AMX) is unavailable;} -#' \item{Inherit ampicillin (AMP) from amoxicillin (AMX), where ampicillin (AMP) is unavailable;} -#' \item{Set amoxicillin (AMX) = R where amoxicillin/clavulanic acid (AMC) = R;} -#' \item{Set piperacillin (PIP) = R where piperacillin/tazobactam (TZP) = R;} -#' \item{Set trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R;} -#' \item{Set amoxicillin/clavulanic acid (AMC) = S where amoxicillin (AMX) = S;} -#' \item{Set piperacillin/tazobactam (TZP) = S where piperacillin (PIP) = S;} -#' \item{Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S.} -#' } -#' To \emph{not} use these rules, please use \code{eucast_rules(..., rules = c("breakpoints", "expert"))}. +#' - Inherit amoxicillin (AMX) from ampicillin (AMP), where amoxicillin (AMX) is unavailable; +#' - Inherit ampicillin (AMP) from amoxicillin (AMX), where ampicillin (AMP) is unavailable; +#' - Set amoxicillin (AMX) = R where amoxicillin/clavulanic acid (AMC) = R; +#' - Set piperacillin (PIP) = R where piperacillin/tazobactam (TZP) = R; +#' - Set trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R; +#' - Set amoxicillin/clavulanic acid (AMC) = S where amoxicillin (AMX) = S; +#' - Set piperacillin/tazobactam (TZP) = S where piperacillin (PIP) = S; +#' - Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S. +#' To *not* use these rules, please use `eucast_rules(..., rules = c("breakpoints", "expert"))`. #' -#' The file containing all EUCAST rules is located here: \url{https://gitlab.com/msberends/AMR/blob/master/data-raw/eucast_rules.tsv}. +#' The file containing all EUCAST rules is located here: . #' #' @section Antibiotics: -#' To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning. +#' To define antibiotics column names, leave as it is to determine it automatically with [guess_ab_col()] or input a text (case-insensitive), or use `NULL` to skip a column (e.g. `TIC = NULL` to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning. #' -#' The following antibiotics are used for the functions \code{\link{eucast_rules}} and \code{\link{mdro}}. These are shown below in the format '\strong{antimicrobial ID}: name (\href{https://www.whocc.no/atc/structure_and_principles/}{ATC code})', sorted by name: +#' The following antibiotics are used for the functions [eucast_rules()] and [mdro()]. These are shown below in the format '**antimicrobial ID**: name ([ATC code](https://www.whocc.no/atc/structure_and_principles/))', sorted by name: #' -#' \strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}), -#' \strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}), -#' \strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}), -#' \strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}), -#' \strong{SAM}: ampicillin/sulbactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR01}{J01CR01}), -#' \strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}), -#' \strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}), -#' \strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}), -#' \strong{CAP}: capreomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB30}{J04AB30}), -#' \strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}), -#' \strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}), -#' \strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}), -#' \strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}), -#' \strong{CTT}: cefotetan (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC05}{J01DC05}), -#' \strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}), -#' \strong{CPT}: ceftaroline (\href{https://www.whocc.no/atc_ddd_index/?code=J01DI02}{J01DI02}), -#' \strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}), -#' \strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}), -#' \strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}), -#' \strong{CED}: cephradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}), -#' \strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}), -#' \strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}), -#' \strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}), -#' \strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}), -#' \strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}), -#' \strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}), -#' \strong{DOR}: doripenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH04}{J01DH04}), -#' \strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}), -#' \strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}), -#' \strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}), -#' \strong{ETH}: ethambutol (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK02}{J04AK02}), -#' \strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}), -#' \strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}), -#' \strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}), -#' \strong{GAT}: gatifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA16}{J01MA16}), -#' \strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}), -#' \strong{GEH}: gentamicin-high (no ATC code), -#' \strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}), -#' \strong{INH}: isoniazid (\href{https://www.whocc.no/atc_ddd_index/?code=J04AC01}{J04AC01}), -#' \strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}), -#' \strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}), -#' \strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}), -#' \strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}), -#' \strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}), -#' \strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01XD01}{J01XD01}), -#' \strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}), -#' \strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}), -#' \strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}), -#' \strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}), -#' \strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}), -#' \strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}), -#' \strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}), -#' \strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}), -#' \strong{NOV}: novobiocin (\href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}), -#' \strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}), -#' \strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF04}{J01CF04}), -#' \strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}), -#' \strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}), -#' \strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}), -#' \strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}), -#' \strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}), -#' \strong{PZA}: pyrazinamide (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK01}{J04AK01}), -#' \strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}), -#' \strong{RIB}: rifabutin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB04}{J04AB04}), -#' \strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}), -#' \strong{RFP}: rifapentine (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB05}{J04AB05}), -#' \strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}), -#' \strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}), -#' \strong{STH}: streptomycin-high (no ATC code), -#' \strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}), -#' \strong{TLV}: telavancin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA03}{J01XA03}), -#' \strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}), -#' \strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}), -#' \strong{TCC}: ticarcillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR03}{J01CR03}), -#' \strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}), -#' \strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}), -#' \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}), -#' \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}), -#' \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}). +#' **AMK**: amikacin ([J01GB06](https://www.whocc.no/atc_ddd_index/?code=J01GB06)), +#' **AMX**: amoxicillin ([J01CA04](https://www.whocc.no/atc_ddd_index/?code=J01CA04)), +#' **AMC**: amoxicillin/clavulanic acid ([J01CR02](https://www.whocc.no/atc_ddd_index/?code=J01CR02)), +#' **AMP**: ampicillin ([J01CA01](https://www.whocc.no/atc_ddd_index/?code=J01CA01)), +#' **SAM**: ampicillin/sulbactam ([J01CR01](https://www.whocc.no/atc_ddd_index/?code=J01CR01)), +#' **AZM**: azithromycin ([J01FA10](https://www.whocc.no/atc_ddd_index/?code=J01FA10)), +#' **AZL**: azlocillin ([J01CA09](https://www.whocc.no/atc_ddd_index/?code=J01CA09)), +#' **ATM**: aztreonam ([J01DF01](https://www.whocc.no/atc_ddd_index/?code=J01DF01)), +#' **CAP**: capreomycin ([J04AB30](https://www.whocc.no/atc_ddd_index/?code=J04AB30)), +#' **RID**: cefaloridine ([J01DB02](https://www.whocc.no/atc_ddd_index/?code=J01DB02)), +#' **CZO**: cefazolin ([J01DB04](https://www.whocc.no/atc_ddd_index/?code=J01DB04)), +#' **FEP**: cefepime ([J01DE01](https://www.whocc.no/atc_ddd_index/?code=J01DE01)), +#' **CTX**: cefotaxime ([J01DD01](https://www.whocc.no/atc_ddd_index/?code=J01DD01)), +#' **CTT**: cefotetan ([J01DC05](https://www.whocc.no/atc_ddd_index/?code=J01DC05)), +#' **FOX**: cefoxitin ([J01DC01](https://www.whocc.no/atc_ddd_index/?code=J01DC01)), +#' **CPT**: ceftaroline ([J01DI02](https://www.whocc.no/atc_ddd_index/?code=J01DI02)), +#' **CAZ**: ceftazidime ([J01DD02](https://www.whocc.no/atc_ddd_index/?code=J01DD02)), +#' **CRO**: ceftriaxone ([J01DD04](https://www.whocc.no/atc_ddd_index/?code=J01DD04)), +#' **CXM**: cefuroxime ([J01DC02](https://www.whocc.no/atc_ddd_index/?code=J01DC02)), +#' **CED**: cephradine ([J01DB09](https://www.whocc.no/atc_ddd_index/?code=J01DB09)), +#' **CHL**: chloramphenicol ([J01BA01](https://www.whocc.no/atc_ddd_index/?code=J01BA01)), +#' **CIP**: ciprofloxacin ([J01MA02](https://www.whocc.no/atc_ddd_index/?code=J01MA02)), +#' **CLR**: clarithromycin ([J01FA09](https://www.whocc.no/atc_ddd_index/?code=J01FA09)), +#' **CLI**: clindamycin ([J01FF01](https://www.whocc.no/atc_ddd_index/?code=J01FF01)), +#' **COL**: colistin ([J01XB01](https://www.whocc.no/atc_ddd_index/?code=J01XB01)), +#' **DAP**: daptomycin ([J01XX09](https://www.whocc.no/atc_ddd_index/?code=J01XX09)), +#' **DOR**: doripenem ([J01DH04](https://www.whocc.no/atc_ddd_index/?code=J01DH04)), +#' **DOX**: doxycycline ([J01AA02](https://www.whocc.no/atc_ddd_index/?code=J01AA02)), +#' **ETP**: ertapenem ([J01DH03](https://www.whocc.no/atc_ddd_index/?code=J01DH03)), +#' **ERY**: erythromycin ([J01FA01](https://www.whocc.no/atc_ddd_index/?code=J01FA01)), +#' **ETH**: ethambutol ([J04AK02](https://www.whocc.no/atc_ddd_index/?code=J04AK02)), +#' **FLC**: flucloxacillin ([J01CF05](https://www.whocc.no/atc_ddd_index/?code=J01CF05)), +#' **FOS**: fosfomycin ([J01XX01](https://www.whocc.no/atc_ddd_index/?code=J01XX01)), +#' **FUS**: fusidic acid ([J01XC01](https://www.whocc.no/atc_ddd_index/?code=J01XC01)), +#' **GAT**: gatifloxacin ([J01MA16](https://www.whocc.no/atc_ddd_index/?code=J01MA16)), +#' **GEN**: gentamicin ([J01GB03](https://www.whocc.no/atc_ddd_index/?code=J01GB03)), +#' **GEH**: gentamicin-high (no ATC code), +#' **IPM**: imipenem ([J01DH51](https://www.whocc.no/atc_ddd_index/?code=J01DH51)), +#' **INH**: isoniazid ([J04AC01](https://www.whocc.no/atc_ddd_index/?code=J04AC01)), +#' **KAN**: kanamycin ([J01GB04](https://www.whocc.no/atc_ddd_index/?code=J01GB04)), +#' **LVX**: levofloxacin ([J01MA12](https://www.whocc.no/atc_ddd_index/?code=J01MA12)), +#' **LIN**: lincomycin ([J01FF02](https://www.whocc.no/atc_ddd_index/?code=J01FF02)), +#' **LNZ**: linezolid ([J01XX08](https://www.whocc.no/atc_ddd_index/?code=J01XX08)), +#' **MEM**: meropenem ([J01DH02](https://www.whocc.no/atc_ddd_index/?code=J01DH02)), +#' **MTR**: metronidazole ([J01XD01](https://www.whocc.no/atc_ddd_index/?code=J01XD01)), +#' **MEZ**: mezlocillin ([J01CA10](https://www.whocc.no/atc_ddd_index/?code=J01CA10)), +#' **MNO**: minocycline ([J01AA08](https://www.whocc.no/atc_ddd_index/?code=J01AA08)), +#' **MFX**: moxifloxacin ([J01MA14](https://www.whocc.no/atc_ddd_index/?code=J01MA14)), +#' **NAL**: nalidixic acid ([J01MB02](https://www.whocc.no/atc_ddd_index/?code=J01MB02)), +#' **NEO**: neomycin ([J01GB05](https://www.whocc.no/atc_ddd_index/?code=J01GB05)), +#' **NET**: netilmicin ([J01GB07](https://www.whocc.no/atc_ddd_index/?code=J01GB07)), +#' **NIT**: nitrofurantoin ([J01XE01](https://www.whocc.no/atc_ddd_index/?code=J01XE01)), +#' **NOR**: norfloxacin ([J01MA06](https://www.whocc.no/atc_ddd_index/?code=J01MA06)), +#' **NOV**: novobiocin ([QJ01XX95](https://www.whocc.no/atc_ddd_index/?code=QJ01XX95)), +#' **OFX**: ofloxacin ([J01MA01](https://www.whocc.no/atc_ddd_index/?code=J01MA01)), +#' **OXA**: oxacillin ([J01CF04](https://www.whocc.no/atc_ddd_index/?code=J01CF04)), +#' **PEN**: penicillin G ([J01CE01](https://www.whocc.no/atc_ddd_index/?code=J01CE01)), +#' **PIP**: piperacillin ([J01CA12](https://www.whocc.no/atc_ddd_index/?code=J01CA12)), +#' **TZP**: piperacillin/tazobactam ([J01CR05](https://www.whocc.no/atc_ddd_index/?code=J01CR05)), +#' **PLB**: polymyxin B ([J01XB02](https://www.whocc.no/atc_ddd_index/?code=J01XB02)), +#' **PRI**: pristinamycin ([J01FG01](https://www.whocc.no/atc_ddd_index/?code=J01FG01)), +#' **PZA**: pyrazinamide ([J04AK01](https://www.whocc.no/atc_ddd_index/?code=J04AK01)), +#' **QDA**: quinupristin/dalfopristin ([J01FG02](https://www.whocc.no/atc_ddd_index/?code=J01FG02)), +#' **RIB**: rifabutin ([J04AB04](https://www.whocc.no/atc_ddd_index/?code=J04AB04)), +#' **RIF**: rifampicin ([J04AB02](https://www.whocc.no/atc_ddd_index/?code=J04AB02)), +#' **RFP**: rifapentine ([J04AB05](https://www.whocc.no/atc_ddd_index/?code=J04AB05)), +#' **RXT**: roxithromycin ([J01FA06](https://www.whocc.no/atc_ddd_index/?code=J01FA06)), +#' **SIS**: sisomicin ([J01GB08](https://www.whocc.no/atc_ddd_index/?code=J01GB08)), +#' **STH**: streptomycin-high (no ATC code), +#' **TEC**: teicoplanin ([J01XA02](https://www.whocc.no/atc_ddd_index/?code=J01XA02)), +#' **TLV**: telavancin ([J01XA03](https://www.whocc.no/atc_ddd_index/?code=J01XA03)), +#' **TCY**: tetracycline ([J01AA07](https://www.whocc.no/atc_ddd_index/?code=J01AA07)), +#' **TIC**: ticarcillin ([J01CA13](https://www.whocc.no/atc_ddd_index/?code=J01CA13)), +#' **TCC**: ticarcillin/clavulanic acid ([J01CR03](https://www.whocc.no/atc_ddd_index/?code=J01CR03)), +#' **TGC**: tigecycline ([J01AA12](https://www.whocc.no/atc_ddd_index/?code=J01AA12)), +#' **TOB**: tobramycin ([J01GB01](https://www.whocc.no/atc_ddd_index/?code=J01GB01)), +#' **TMP**: trimethoprim ([J01EA01](https://www.whocc.no/atc_ddd_index/?code=J01EA01)), +#' **SXT**: trimethoprim/sulfamethoxazole ([J01EE01](https://www.whocc.no/atc_ddd_index/?code=J01EE01)), +#' **VAN**: vancomycin ([J01XA01](https://www.whocc.no/atc_ddd_index/?code=J01XA01)). #' @aliases EUCAST #' @rdname eucast_rules #' @export #' @importFrom dplyr %>% select pull mutate_at vars group_by summarise n #' @importFrom crayon bold bgGreen bgYellow bgRed black green blue italic strip_style white red make_style #' @importFrom utils menu -#' @return The input of \code{x}, possibly with edited values of antibiotics. Or, if \code{verbose = TRUE}, a \code{data.frame} with all original and new values of the affected bug-drug combinations. +#' @return The input of `x`, possibly with edited values of antibiotics. Or, if `verbose = TRUE`, a [`data.frame`] with all original and new values of the affected bug-drug combinations. #' @source -#' \itemize{ -#' \item{ -#' EUCAST Expert Rules. Version 2.0, 2012. \cr -#' Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr -#' \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} -#' } -#' \item{ -#' EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. \cr -#' \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf} -#' } -#' \item{ -#' EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. \cr -#' \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx} -#' } -#' } +#' - EUCAST Expert Rules. Version 2.0, 2012. \cr +#' Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60. \cr +#' +#' - EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016. \cr +#' +#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. \cr +#' #' @inheritSection AMR Read more on our website! #' @examples #' \donttest{ @@ -548,7 +538,7 @@ eucast_rules <- function(x, streptogramins <- c(QDA, PRI) # should officially also be quinupristin/dalfopristin aminopenicillins <- c(AMP, AMX) cephalosporins <- c(FEP, CTX, FOX, CED, CAZ, CRO, CXM, CZO) - cephalosporins_without_CAZ <- cephalosporins[cephalosporins != ifelse(is.null(CAZ), "", CAZ)] + cephalosporins_except_CAZ <- cephalosporins[cephalosporins != ifelse(is.null(CAZ), "", CAZ)] carbapenems <- c(ETP, IPM, MEM) ureidopenicillins <- c(PIP, TZP, AZL, MEZ) all_betalactams <- c(aminopenicillins, cephalosporins, carbapenems, ureidopenicillins, AMC, OXA, FLC, PEN) @@ -568,13 +558,16 @@ eucast_rules <- function(x, y[y != "" & y %in% colnames(df)] } get_antibiotic_names <- function(x) { - x %>% + x <- x %>% strsplit(",") %>% unlist() %>% trimws() %>% sapply(function(x) if (x %in% AMR::antibiotics$ab) ab_name(x, language = NULL, tolower = TRUE) else x) %>% sort() %>% paste(collapse = ", ") + x <- gsub("_", " ", x, fixed = TRUE) + x <- gsub("except CAZ", paste("except", ab_name("CAZ", language = NULL, tolower = TRUE)), x, fixed = TRUE) + x } format_antibiotic_names <- function(ab_names, ab_results) { ab_names <- trimws(unlist(strsplit(ab_names, ","))) diff --git a/R/filter_ab_class.R b/R/filter_ab_class.R index 1ab7f9a1f..3c3ecedc2 100644 --- a/R/filter_ab_class.R +++ b/R/filter_ab_class.R @@ -23,11 +23,11 @@ #' #' Filter isolates on results in specific antibiotic variables based on their class (ATC groups). This makes it easy to get a list of isolates that were tested for e.g. any aminoglycoside. #' @param x a data set -#' @param ab_class an antimicrobial class, like \code{"carbapenems"}, as can be found in \code{AMR::antibiotics$group} +#' @param ab_class an antimicrobial class, like `"carbapenems"`, as can be found in [`AMR::antibiotics$group`][antibiotics] #' @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 \code{"any"} (default) or \code{"all"} -#' @param ... parameters passed on to \code{filter_at} from the \code{dplyr} package -#' @details The \code{group} column in \code{\link{antibiotics}} data set will be searched for \code{ab_class} (case-insensitive). If no results are found, the \code{atc_group1} and \code{atc_group2} columns will be searched. Next, \code{x} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set. +#' @param scope the scope to check which variables to check, can be `"any"` (default) or `"all"` +#' @param ... parameters passed on to `filter_at` from the `dplyr` package +#' @details The `group` column in [antibiotics] data set will be searched for `ab_class` (case-insensitive). If no results are found, the `atc_group1` and `atc_group2` columns will be searched. Next, `x` will be checked for column names with a value in any abbreviations, codes or official names found in the [antibiotics] data set. #' @rdname filter_ab_class #' @importFrom dplyr filter_at %>% select vars any_vars all_vars #' @importFrom crayon bold blue diff --git a/R/first_isolate.R b/R/first_isolate.R index 50f15d846..fa4631608 100755 --- a/R/first_isolate.R +++ b/R/first_isolate.R @@ -22,61 +22,65 @@ #' Determine first (weighted) isolates #' #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. -#' @param x a \code{data.frame} containing isolates. +#' @param x a [`data.frame`] containing isolates. #' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class #' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive) -#' @param col_mo column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}. -#' @param col_testcode column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. +#' @param col_mo column name of the IDs of the microorganisms (see [as.mo()]), defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()]. +#' @param col_testcode column name of the test codes. Use `col_testcode = NULL` to **not** exclude certain test codes (like test codes for screening). In that case `testcodes_exclude` will be ignored. #' @param col_specimen column name of the specimen type or group -#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU) -#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this. +#' @param col_icu column name of the logicals (`TRUE`/`FALSE`) whether a ward or department is an Intensive Care Unit (ICU) +#' @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 \code{TRUE} in column \code{col_icu}) -#' @param specimen_group value in column \code{col_specimen} to filter on -#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details -#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details -#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details +#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value `TRUE` in column `col_icu`) +#' @param specimen_group value in column `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 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 \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. -#' @param ... parameters passed on to the \code{first_isolate} function -#' @details \strong{WHY THIS IS SO IMPORTANT} \cr -#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. +#' @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 ... parameters passed on to the [first_isolate()] function +#' @details **WHY THIS IS SO IMPORTANT** \cr +#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [[1]](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias). #' -#' All isolates with a microbial ID of \code{NA} will be excluded as first isolate. +#' All isolates with a microbial ID of `NA` will be excluded as first isolate. #' -#' The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to: -#' \preformatted{ -#' x \%>\% -#' mutate(only_firsts = first_isolate(x, ...)) \%>\% -#' filter(only_firsts == TRUE) \%>\% +#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to: +#' ``` +#' x %>% +#' mutate(only_firsts = first_isolate(x, ...)) %>% +#' filter(only_firsts == TRUE) %>% #' select(-only_firsts) -#' } -#' The function \code{filter_first_weighted_isolate} is essentially equal to: -#' \preformatted{ -#' x \%>\% -#' mutate(keyab = key_antibiotics(.)) \%>\% +#' ``` +#' The function [filter_first_weighted_isolate()] is essentially equal to: +#' ``` +#' x %>% +#' mutate(keyab = key_antibiotics(.)) %>% #' mutate(only_weighted_firsts = first_isolate(x, -#' col_keyantibiotics = "keyab", ...)) \%>\% -#' filter(only_weighted_firsts == TRUE) \%>\% +#' col_keyantibiotics = "keyab", ...)) %>% +#' filter(only_weighted_firsts == TRUE) %>% #' select(-only_weighted_firsts) -#' } +#' ``` #' @section Key antibiotics: -#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr +#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results: #' -#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr -#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr -#' -#' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr -#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate. +#' 1. Using `type = "keyantibiotics"` and parameter `ignore_I` +#' +#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With `ignore_I = FALSE`, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the [key_antibiotics()] function. +#' +#' 2. Using `type = "points"` and parameter `points_threshold` +#' +#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds `points_threshold`, which default to `2`, an isolate will be (re)selected as a first weighted isolate. #' @rdname first_isolate -#' @seealso \code{\link{key_antibiotics}} +#' @seealso [key_antibiotics()] #' @export #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange pull ungroup #' @importFrom crayon blue bold silver # @importFrom clean percentage -#' @return Logical vector -#' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. +#' @return A [`logical`] vector +#' @source Methodology of this function is based on: +#' +#' **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. . #' @inheritSection AMR Read more on our website! #' @examples #' # `example_isolates` is a dataset available in the AMR package. diff --git a/R/g.test.R b/R/g.test.R index 5ccad1166..7d644c253 100755 --- a/R/g.test.R +++ b/R/g.test.R @@ -19,56 +19,56 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' \emph{G}-test for Count Data +#' *G*-test for Count Data #' -#' \code{g.test} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link{chisq.test}} but is more reliable [1]. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}). +#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable [1]. A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**). #' @inherit stats::chisq.test params return -#' @details If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given. +#' @details If `x` is a matrix with one row or column, or if `x` is a vector and `y` is not given, then a *goodness-of-fit test* is performed (`x` is treated as a one-dimensional contingency table). The entries of `x` must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in `p`, or are all equal if `p` is not given. #' -#' If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers. Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals. +#' If `x` is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of `x` must be non-negative integers. Otherwise, `x` and `y` must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals. #' -#' The p-value is computed from the asymptotic chi-squared distribution of the test statistic. +#' The p-value is computed from the asymptotic chi-squared distribution of the test statistic. #' -#' In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test. +#' In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the *G*-test) but rather that for Fisher's exact test. #' -#' In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow. -#' @section \emph{G}-test of goodness-of-fit (likelihood ratio test): -#' Use the \emph{G}-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross). +#' In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by `p`, each sample being of size `n = sum(x)`. This simulation is done in \R and may be slow. +#' +#' ## *G*-test of goodness-of-fit (likelihood ratio test) +#' Use the *G*-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross). #' -#' If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link{fisher.test}}). +#' If the expected number of observations in any category is too small, the *G*-test may give inaccurate results, and you should use an exact test instead ([fisher.test()]). #' -#' The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link{chisq.test}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar. +#' The *G*-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit ([chisq.test()]); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar. #' -#' @section \emph{G}-test of independence: -#' Use the \emph{G}-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable. +#' ## *G*-test of independence +#' Use the *G*-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable. #' -#' It is also possible to do a \emph{G}-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together. +#' It is also possible to do a *G*-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together. #' -#' Fisher's exact test (\code{\link{fisher.test}}) is an \strong{exact} test, where the \emph{G}-test is still only an \strong{approximation}. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions. +#' Fisher's exact test ([fisher.test()]) is an **exact** test, where the *G*-test is still only an **approximation**. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions. #' -#' The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link{chisq.test}}), and they will give approximately the same results. -#' @section How the test works: -#' Unlike the exact test of goodness-of-fit (\code{\link{fisher.test}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic. +#' The *G*-test of independence is an alternative to the chi-square test of independence ([chisq.test()]), and they will give approximately the same results. #' -#' The \emph{G}-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a \emph{G}-statistic is: +#' ## How the test works +#' Unlike the exact test of goodness-of-fit ([fisher.test()]), the *G*-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the *G*-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic. #' -#' \code{G <- 2 * sum(x * log(x / E))} +#' The *G*-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a *G*-statistic is: +#' +#' \eqn{G = 2 * sum(x * log(x / E))} +#' +#' where `E` are the expected values. Since this is chi-square distributed, the p value can be calculated in \R with: +#' ``` +#' p <- stats::pchisq(G, df, lower.tail = FALSE) +#' ``` +#' where `df` are the degrees of freedom. #' -#' where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated with: -#' -#' \code{p <- stats::pchisq(G, df, lower.tail = FALSE)} -#' -#' where \code{df} are the degrees of freedom. -#' -#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course. -#' @seealso \code{\link{chisq.test}} -#' @references [1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}. -#' @source This code is almost identical to \code{\link{chisq.test}}, except that: -#' \itemize{ -#' \item{The calculation of the statistic was changed to \code{2 * sum(x * log(x / E))}} -#' \item{Yates' continuity correction was removed as it does not apply to a \emph{G}-test} -#' \item{The possibility to simulate p values with \code{simulate.p.value} was removed} -#' } +#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use *G*-tests for each category, of course. +#' @seealso [chisq.test()] +#' @references [1] McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. . +#' @source The code for this function is identical to that of [chisq.test()], except that: +#' - The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))} +#' - Yates' continuity correction was removed as it does not apply to a *G*-test +#' - The possibility to simulate p values with `simulate.p.value` was removed #' @export #' @importFrom stats pchisq complete.cases #' @inheritSection AMR Read more on our website! diff --git a/R/ggplot_rsi.R b/R/ggplot_rsi.R index 62eb7074b..e194a6437 100755 --- a/R/ggplot_rsi.R +++ b/R/ggplot_rsi.R @@ -19,20 +19,20 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' AMR plots with \code{ggplot2} +#' AMR plots with `ggplot2` #' -#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}2} functions. -#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}}) -#' @param position position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"} -#' @param x variable to show on x axis, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable -#' @param fill variable to categorise using the plots legend, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable +#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal [ggplot2][ggplot2::ggplot()] functions. +#' @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 +#' @param fill variable to categorise using the plots legend, either `"antibiotic"` (default) or `"interpretation"` or a grouping variable #' @param breaks numeric vector of positions -#' @param limits numeric vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum -#' @param facet variable to split plots by, either \code{"interpretation"} (default) or \code{"antibiotic"} or a grouping variable +#' @param limits numeric vector of length two providing limits of the scale, use `NA` to refer to the existing minimum or maximum +#' @param facet variable to split plots by, either `"interpretation"` (default) or `"antibiotic"` or a grouping variable #' @inheritParams proportion -#' @param nrow (when using \code{facet}) number of rows -#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} to use default \code{ggplot2} colours. -#' @param datalabels show datalabels using \code{labels_rsi_count} +#' @param nrow (when using `facet`) number of rows +#' @param colours a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be `FALSE` to use default [ggplot2][[ggplot2::ggplot()] colours. +#' @param datalabels show datalabels using [labels_rsi_count()] #' @param datalabels.size size of the datalabels #' @param datalabels.colour colour of the datalabels #' @param title text to show as title of the plot @@ -40,23 +40,23 @@ #' @param caption text to show as caption of the plot #' @param x.title text to show as x axis description #' @param y.title text to show as y axis description -#' @param ... other parameters passed on to \code{geom_rsi} -#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{ab_name}}. This can be set with the \code{translate_ab} parameter. See \code{\link{count_df}}. +#' @param ... other parameters passed on to [geom_rsi()] +#' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` parameter. See [count_df()]. #' -#' \strong{The functions}\cr -#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{rsi_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis. +#' ## The functions +#' [geom_rsi()] will take any variable from the data that has an [`rsi`] class (created with [as.rsi()]) using [rsi_df()] and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis. #' -#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}. +#' [facet_rsi()] creates 2d plots (at default based on S/I/R) using [ggplot2::facet_wrap()]. #' -#' \code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}. +#' [scale_y_percent()] transforms the y axis to a 0 to 100% range using [ggplot2::scale_continuous()]. #' -#' \code{scale_rsi_colours} sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using \code{\link[ggplot2]{scale_brewer}}. +#' [scale_rsi_colours()] sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using [ggplot2::scale_brewer()]. #' -#' \code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction. +#' [theme_rsi()] is a [ggplot2 theme][[ggplot2::theme()] with minimal distraction. #' -#' \code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}} +#' [labels_rsi_count()] print datalabels on the bars with percentage and amount of isolates using [ggplot2::geom_text()] #' -#' \code{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 (\code{\%>\%}). See Examples. +#' [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 (`%>%`). See Examples. #' @rdname ggplot_rsi #' @export #' @inheritSection AMR Read more on our website! diff --git a/R/guess_ab_col.R b/R/guess_ab_col.R index faedfbc65..2c87cdb99 100755 --- a/R/guess_ab_col.R +++ b/R/guess_ab_col.R @@ -21,14 +21,14 @@ #' Guess antibiotic column #' -#' This tries to find a column name in a data set based on information from the \code{\link{antibiotics}} data set. Also supports WHONET abbreviations. -#' @param x a \code{data.frame} -#' @param search_string a text to search \code{x} for, will be checked with \code{\link{as.ab}} if this value is not a column in \code{x} +#' This tries to find a column name in a data set based on information from the [antibiotics] data set. Also supports WHONET abbreviations. +#' @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 -#' @details You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \code{\link{antibiotics}} data set for any column containing a name or code of that antibiotic. \strong{Longer columns names take precendence over shorter column names.} +#' @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 precendence over shorter column names.** #' @importFrom dplyr %>% select filter_all any_vars #' @importFrom crayon blue -#' @return A column name of \code{x}, or \code{NULL} when no result is found. +#' @return A column name of `x`, or `NULL` when no result is found. #' @export #' @inheritSection AMR Read more on our website! #' @examples diff --git a/R/join_microorganisms.R b/R/join_microorganisms.R index d504bea02..fc1946e0b 100755 --- a/R/join_microorganisms.R +++ b/R/join_microorganisms.R @@ -19,17 +19,17 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' Join a table with \code{microorganisms} +#' Join a table with [microorganisms] #' -#' Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector. +#' Join the data set [microorganisms] easily to an existing table or character vector. #' @rdname join #' @name join #' @aliases join inner_join #' @param x existing table to join, or character vector -#' @param by a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")}) -#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2. -#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}. -#' @details \strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information. +#' @param by a variable to join by - if left empty will search for a column with class [`mo`] (created with [as.mo()]) or will be `"mo"` if that column name exists in `x`, could otherwise be a column name of `x` with values that exist in `microorganisms$mo` (like `by = "bacteria_id"`), or another column in [microorganisms] (but then it should be named, like `by = c("my_genus_species" = "fullname")`) +#' @param suffix if there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2. +#' @param ... other parameters to pass on to [dplyr::join()] +#' @details **Note:** As opposed to the [dplyr::join()] functions of `dplyr`, [`characters`] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. See [dplyr::join()] for more information. #' @inheritSection AMR Read more on our website! #' @export #' @examples diff --git a/R/key_antibiotics.R b/R/key_antibiotics.R index a62a9da01..afb52233f 100755 --- a/R/key_antibiotics.R +++ b/R/key_antibiotics.R @@ -19,33 +19,54 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' Key antibiotics for first \emph{weighted} isolates +#' Key antibiotics for first *weighted* isolates #' -#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates. -#' @param x table with antibiotics coloms, like \code{AMX} or \code{amox} +#' These function can be used to determine first isolates (see [first_isolate()]). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first *weighted* isolates. +#' @param x table with antibiotics coloms, like `AMX` or `amox` #' @param y,z characters to compare #' @inheritParams first_isolate -#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}. -#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram-positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}. -#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram-negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}. +#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of **broad-spectrum** antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()]. +#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for **Gram-positives**, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()]. +#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for **Gram-negatives**, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()]. #' @param warnings give warning about missing antibiotic columns, they will anyway be ignored #' @param ... other parameters passed on to function -#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not. +#' @details The function [key_antibiotics()] returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using [key_antibiotics_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`). The [first_isolate()] function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible *S. aureus* (MSSA) found within the same episode (see `episode` parameter of [first_isolate()]). Without key antibiotic comparison it would not. #' -#' At default, the antibiotics that are used for \strong{Gram-positive bacteria} are: \cr -#' amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampin. +#' At default, the antibiotics that are used for **Gram-positive bacteria** are: +#' - Amoxicillin +#' - Amoxicillin/clavulanic acid +#' - Cefuroxime +#' - Piperacillin/tazobactam +#' - Ciprofloxacin +#' - Trimethoprim/sulfamethoxazole +#' - Vancomycin +#' - Teicoplanin +#' - Tetracycline +#' - Erythromycin +#' - Oxacillin +#' - Rifampin #' -#' At default, the antibiotics that are used for \strong{Gram-negative bacteria} are: \cr -#' amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem. -#' -#' -#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector. +#' At default the antibiotics that are used for **Gram-negative bacteria** are: +#' - Amoxicillin +#' - Amoxicillin/clavulanic acid +#' - Cefuroxime +#' - Piperacillin/tazobactam +#' - Ciprofloxacin +#' - Trimethoprim/sulfamethoxazole +#' - Gentamicin +#' - Tobramycin +#' - Colistin +#' - Cefotaxime +#' - Ceftazidime +#' - Meropenem +#' +#' The function [key_antibiotics_equal()] checks the characters returned by [key_antibiotics()] for equality, and returns a [`logical`] vector. #' @inheritSection first_isolate Key antibiotics #' @rdname key_antibiotics #' @export #' @importFrom dplyr %>% mutate if_else pull #' @importFrom crayon blue bold -#' @seealso \code{\link{first_isolate}} +#' @seealso [first_isolate()] #' @inheritSection AMR Read more on our website! #' @examples #' # `example_isolates` is a dataset available in the AMR package. diff --git a/R/kurtosis.R b/R/kurtosis.R index aa0f41509..b44f2e6d3 100755 --- a/R/kurtosis.R +++ b/R/kurtosis.R @@ -22,11 +22,10 @@ #' Kurtosis of the sample #' #' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. -#' -#' @param x a vector of values, a \code{matrix} or a \code{data frame} -#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds. +#' @param x a vector of values, a [`matrix`] or a [`data frame`] +#' @param na.rm a logical value indicating whether `NA` values should be stripped before the computation proceeds. #' @exportMethod kurtosis -#' @seealso \code{\link{skewness}} +#' @seealso [skewness()] #' @rdname kurtosis #' @inheritSection AMR Read more on our website! #' @export diff --git a/R/like.R b/R/like.R index 9ba97a2ef..7ab7b6233 100755 --- a/R/like.R +++ b/R/like.R @@ -21,15 +21,15 @@ #' Pattern Matching #' -#' Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive (use \code{a \%like_case\% b} for case-sensitive matching). Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors, or can both have the same length to iterate over all cases. +#' Convenient wrapper around [base::grep()] to match a pattern: `a %like% b`. It always returns a [`logical`] vector and is always case-insensitive (use `a %like_case% b` for case-sensitive matching). Also, `pattern` (*b*) can be as long as `x` (*a*) to compare items of each index in both vectors, or can both have the same length to iterate over all cases. #' @inheritParams base::grepl -#' @return A \code{logical} vector +#' @return A [`logical`] vector #' @name like #' @rdname like #' @export -#' @details Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...). -#' @source Idea from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default and let it support multiple patterns. Also, if the regex fails the first time, it tries again with \code{perl = TRUE}. -#' @seealso \code{\link[base]{grep}} +#' @details Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like `Ctrl+Shift+L` or `Cmd+Shift+L` (see `Tools` > `Modify Keyboard Shortcuts...`). +#' @source Idea from the [`like` function from the `data.table` package](https://github.com/Rdatatable/data.table/blob/master/R/like.R), but made it case insensitive at default and let it support multiple patterns. Also, if the regex fails the first time, it tries again with `perl = TRUE`. +#' @seealso [base::grep()] #' @inheritSection AMR Read more on our website! #' @examples #' # simple test diff --git a/R/mdro.R b/R/mdro.R index 7a923c1a0..867eadc5e 100755 --- a/R/mdro.R +++ b/R/mdro.R @@ -22,35 +22,42 @@ #' Determine multidrug-resistant organisms (MDRO) #' #' Determine which isolates are multidrug-resistant organisms (MDRO) according to international and national guidelines. -#' @param guideline a specific guideline to follow. When left empty, the publication by Magiorakos \emph{et al.} (2012, Clinical Microbiology and Infection) will be followed, see Details. -#' @param info print progress +#' @param guideline a specific guideline to follow. When left empty, the publication by Magiorakos *et al.* (2012, Clinical Microbiology and Infection) will be followed, please see *Details*. +#' @param info a logical to indicate whether progress should be printed to the console #' @inheritParams eucast_rules -#' @param pct_required_classes minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for \emph{S. aureus}. Setting this \code{pct_required_classes} argument to \code{0.5} (default) means that for every \emph{S. aureus} isolate at least 8 different classes must be available. Any lower number of available classes will return \code{NA} for that isolate. -#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the \code{mdro()} function, it follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using \code{combine_SI = FALSE}, resistance is considered when isolates are R or I. +#' @param pct_required_classes minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for *S. aureus*. Setting this `pct_required_classes` argument to `0.5` (default) means that for every *S. aureus* isolate at least 8 different classes must be available. Any lower number of available classes will return `NA` for that isolate. +#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the [mdro()] function, it follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using `combine_SI = FALSE`, resistance is considered when isolates are R or I. #' @param verbose a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not. #' @inheritSection eucast_rules Antibiotics #' @details -#' For the \code{pct_required_classes} argument, values above 1 will be divided by 100. This is to support both fractions (\code{0.75} or \code{3/4}) and percentages (\code{75}). +#' For the `pct_required_classes` argument, values above 1 will be divided by 100. This is to support both fractions (`0.75` or `3/4`) and percentages (`75`). #' #' Currently supported guidelines are (case-insensitive): -#' \itemize{ -#' \item{\code{guideline = "CMI2012"}: Magiorakos AP, Srinivasan A \emph{et al.} "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (\href{https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext}{link})} -#' \item{\code{guideline = "EUCAST"}: The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\href{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{link})} -#' \item{\code{guideline = "TB"}: The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (\href{https://www.who.int/tb/publications/pmdt_companionhandbook/en/}{link})} -#' \item{\code{guideline = "MRGN"}: The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6} -#' \item{\code{guideline = "BRMO"}: The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" (\href{https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH}{link})} -#' } -#' -#' Please suggest your own (country-specific) guidelines by letting us know: \url{https://gitlab.com/msberends/AMR/issues/new}. #' -#' \strong{Note:} Every test that involves the Enterobacteriaceae family, will internally be performed using its newly named order Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeolu \emph{et al.} in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by this \code{mdro()} function makes sure that results from before 2016 and after 2016 are identical. +#' - `guideline = "CMI2012"`\cr +#' Magiorakos AP, Srinivasan A *et al.* "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) ([link](https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext)) +#' - `guideline = "EUCAST"`\cr +#' The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" ([link](http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf)) +#' - `guideline = "TB"`\cr +#' The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" ([link](https://www.who.int/tb/publications/pmdt_companionhandbook/en/)) +#' - `guideline = "MRGN"`\cr +#' The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 +#' - `guideline = "BRMO"`\cr +#' The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" ([link](https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH)) +#' +#' Please suggest your own (country-specific) guidelines by letting us know: . +#' +#' **Note:** Every test that involves the Enterobacteriaceae family, will internally be performed using its newly named order Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeolu *et al.* in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by this [mdro()] function makes sure that results from before 2016 and after 2016 are identical. #' @inheritSection as.rsi Interpretation of S, I and R -#' @return \itemize{ -#' \item{CMI 2012 paper - function \code{mdr_cmi2012()} or \code{mdro()}:\cr Ordered factor with levels \code{Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)}} -#' \item{TB guideline - function \code{mdr_tb()} or \code{mdro(..., guideline = "TB")}:\cr Ordered factor with levels \code{Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant}} -#' \item{German guideline - function \code{mrgn()} or \code{mdro(..., guideline = "MRGN")}:\cr Ordered factor with levels \code{Negative < 3MRGN < 4MRGN}} -#' \item{Everything else:\cr Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}. The value \code{"Positive, unconfirmed"} means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests} -#' } +#' @return +#' - CMI 2012 paper - function [mdr_cmi2012()] or [mdro()]:\cr +#' Ordered [`factor`] with levels `Negative` < `Multi-drug-resistant (MDR)` < `Extensively drug-resistant (XDR)` < `Pandrug-resistant (PDR)` +#' - TB guideline - function [mdr_tb()] or [`mdro(..., guideline = "TB")`][mdro()]:\cr +#' Ordered [`factor`] with levels `Negative` < `Mono-resistant` < `Poly-resistant` < `Multi-drug-resistant` < `Extensively drug-resistant` +#' - German guideline - function [mrgn()] or [`mdro(..., guideline = "MRGN")`][mdro()]:\cr +#' Ordered [`factor`] with levels `Negative` < `3MRGN` < `4MRGN` +#' - Everything else:\cr +#' Ordered [`factor`] with levels `Negative` < `Positive, unconfirmed` < `Positive`. The value `"Positive, unconfirmed"` means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests #' @rdname mdro #' @aliases MDR XDR PDR BRMO 3MRGN 4MRGN #' @importFrom dplyr %>% filter_at vars all_vars pull mutate_at @@ -59,7 +66,7 @@ #' @export #' @inheritSection AMR Read more on our website! #' @source -#' Please see Details for the list of publications used for this function. +#' Please see *Details* for the list of publications used for this function. #' @examples #' \donttest{ #' library(dplyr) @@ -74,7 +81,7 @@ #' MRGN = mrgn(.)) #' } mdro <- function(x, - guideline = NULL, + guideline = "CMI2012", col_mo = NULL, info = TRUE, pct_required_classes = 0.5, diff --git a/R/mic.R b/R/mic.R index dcdd17bc7..8c0a0c2ac 100755 --- a/R/mic.R +++ b/R/mic.R @@ -21,16 +21,16 @@ #' Class 'mic' #' -#' This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning. +#' This transforms a vector to a new class [`mic`], which is an ordered [`factor`] with valid MIC values as levels. Invalid MIC values will be translated as `NA` with a warning. #' @rdname as.mic #' @param x vector #' @param na.rm a logical indicating whether missing values should be removed -#' @details Interpret MIC values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI. -#' @return Ordered factor with new class \code{mic} +#' @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 new class [`mic`] #' @aliases MIC #' @export #' @importFrom dplyr %>% -#' @seealso \code{\link{as.rsi}} +#' @seealso [as.rsi()] #' @inheritSection AMR Read more on our website! #' @examples #' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16")) diff --git a/R/mo.R b/R/mo.R index b310c0c08..28bd5abb2 100755 --- a/R/mo.R +++ b/R/mo.R @@ -21,24 +21,25 @@ #' Transform to microorganism ID #' -#' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. Please see Examples. -#' @param x a character vector or a \code{data.frame} with one or two columns -#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into coagulase-negative \emph{Staphylococci} ("CoNS") and coagulase-positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1,2]. Note that this does not include species that were newly named after these publications, like \emph{S. caeli}. +#' Use this function to determine a valid microorganism ID ([`mo`]). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like `"Staphylococcus aureus"`), an abbreviated name (like `"S. aureus"`), an abbreviation known in the field (like `"MRSA"`), or just a genus. Please see *Examples*. +#' @param x a character vector or a [`data.frame`] with one or two columns +#' @param Becker a logical to indicate whether *Staphylococci* should be categorised into coagulase-negative *Staphylococci* ("CoNS") and coagulase-positive *Staphylococci* ("CoPS") instead of their own species, according to Karsten Becker *et al.* (1,2). Note that this does not include species that were newly named after these publications, like *S. caeli*. #' -#' This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS". -#' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [3]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. +#' This excludes *Staphylococcus aureus* at default, use `Becker = "all"` to also categorise *S. aureus* as "CoPS". +#' @param Lancefield a logical to indicate whether beta-haemolytic *Streptococci* should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (3). These *Streptococci* will be categorised in their first group, e.g. *Streptococcus dysgalactiae* will be group C, although officially it was also categorised into groups G and L. #' -#' This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D. -#' @param allow_uncertain a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, see Details -#' @param reference_df a \code{data.frame} to use for extra reference when translating \code{x} to a valid \code{mo}. See \code{\link{set_mo_source}} and \code{\link{get_mo_source}} to automate the usage of your own codes (e.g. used in your analysis or organisation). +#' This excludes *Enterococci* at default (who are in group D), use `Lancefield = "all"` to also categorise all *Enterococci* as group D. +#' @param allow_uncertain a number between `0` (or `"none"`) and `3` (or `"all"`), or `TRUE` (= `2`) or `FALSE` (= `0`) to indicate whether the input should be checked for less probable results, please see *Details* +#' @param reference_df a [`data.frame`] to use for extra reference when translating `x` to a valid [`mo`]. See [set_mo_source()] and [get_mo_source()] to automate the usage of your own codes (e.g. used in your analysis or organisation). #' @param ... other parameters passed on to functions #' @rdname as.mo #' @aliases mo #' @keywords mo Becker becker Lancefield lancefield guess #' @details -#' \strong{General info} \cr -#' A microorganism ID from this package (class: \code{mo}) typically looks like these examples:\cr -#' \preformatted{ +#' ## General info +#' +#' A microorganism ID from this package (class: [`mo`]) typically looks like these examples: +#' ``` #' Code Full name #' --------------- -------------------------------------- #' B_KLBSL Klebsiella @@ -51,81 +52,71 @@ #' | ----> genus, a 5-7 letter acronym #' ----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria), #' C (Chromista), F (Fungi), P (Protozoa) -#' } +#' ``` #' -#' Values that cannot be coered will be considered 'unknown' and will get the MO code \code{UNKNOWN}. +#' Values that cannot be coered will be considered 'unknown' and will get the MO code `UNKNOWN`. #' -#' Use the \code{\link{mo_property}_*} functions to get properties based on the returned code, see Examples. +#' Use the [`mo_property_*`][mo_property()] functions to get properties based on the returned code, see Examples. #' -#' The algorithm uses data from the Catalogue of Life (see below) and from one other source (see \code{\link{microorganisms}}). +#' The algorithm uses data from the Catalogue of Life (see below) and from one other source (see [microorganisms]). #' -#' The \code{as.mo()} function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order: - -#' \itemize{ -#' \item{Human pathogenic prevalence: the function starts with more prevalent microorganisms, followed by less prevalent ones;} -#' \item{Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;} -#' \item{Breakdown of input values to identify possible matches.} -#' } -#' -#' This will lead to the effect that e.g. \code{"E. coli"} (a highly prevalent microorganism found in humans) will return the microbial ID of \emph{Escherichia coli} and not \emph{Entamoeba coli} (a less prevalent microorganism in humans), although the latter would alphabetically come first. +#' The [as.mo()] function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order: #' -#' \strong{Coping with uncertain results} \cr -#' In addition, the \code{as.mo()} function can differentiate four levels of uncertainty to guess valid results: +#' 1. Human pathogenic prevalence: the function starts with more prevalent microorganisms, followed by less prevalent ones; +#' 2. Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others; +#' 3. Breakdown of input values to identify possible matches. +#' +#' This will lead to the effect that e.g. `"E. coli"` (a highly prevalent microorganism found in humans) will return the microbial ID of *Escherichia coli* and not *Entamoeba coli* (a less prevalent microorganism in humans), although the latter would alphabetically come first. #' -#' \itemize{ -#' \item{Uncertainty level 0: no additional rules are applied;} -#' \item{Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;} -#' \item{Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;} -#' \item{Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.} -#' } +#' ## Coping with uncertain results +#' +#' In addition, the [as.mo()] function can differentiate four levels of uncertainty to guess valid results: +#' - Uncertainty level 0: no additional rules are applied; +#' - Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors; +#' - Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements; +#' - Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name. #' #' This leads to e.g.: -#' -#' \itemize{ -#' \item{\code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRPB}) needs review.} -#' \item{\code{"S. aureus - please mind: MRSA"}. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result \emph{Staphylococcus aureus} (\code{B_STPHY_AURS}) needs review.} -#' \item{\code{"Fluoroquinolone-resistant Neisseria gonorrhoeae"}. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result \emph{Neisseria gonorrhoeae} (\code{B_NESSR_GNRR}) needs review.} -#' } +#' - `"Streptococcus group B (known as S. agalactiae)"`. The text between brackets will be removed and a warning will be thrown that the result *Streptococcus group B* (`B_STRPT_GRPB`) needs review. +#' - `"S. aureus - please mind: MRSA"`. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result *Staphylococcus aureus* (`B_STPHY_AURS`) needs review. +#' - `"Fluoroquinolone-resistant Neisseria gonorrhoeae"`. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result *Neisseria gonorrhoeae* (`B_NESSR_GNRR`) needs review. #' -#' The level of uncertainty can be set using the argument \code{allow_uncertain}. The default is \code{allow_uncertain = TRUE}, which is equal to uncertainty level 2. Using \code{allow_uncertain = FALSE} is equal to uncertainty level 0 and will skip all rules. You can also use e.g. \code{as.mo(..., allow_uncertain = 1)} to only allow up to level 1 uncertainty. +#' The level of uncertainty can be set using the argument `allow_uncertain`. The default is `allow_uncertain = TRUE`, which is equal to uncertainty level 2. Using `allow_uncertain = FALSE` is equal to uncertainty level 0 and will skip all rules. You can also use e.g. `as.mo(..., allow_uncertain = 1)` to only allow up to level 1 uncertainty. #' -#' There are three helper functions that can be run after then \code{as.mo()} function: -#' \itemize{ -#' \item{Use \code{mo_uncertainties()} to get a \code{data.frame} with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \code{(n - 0.5 * L) / n}, where \emph{n} is the number of characters of the returned full name of the microorganism, and \emph{L} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} between that full name and the user input.} -#' \item{Use \code{mo_failures()} to get a vector with all values that could not be coerced to a valid value.} -#' \item{Use \code{mo_renamed()} to get a \code{data.frame} with all values that could be coerced based on an old, previously accepted taxonomic name.} -#' } +#' There are three helper functions that can be run after then [as.mo()] function: +#' - Use [mo_uncertainties()] to get a [`data.frame`] with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \eqn{(n - 0.5 * L) / n}, where *n* is the number of characters of the returned full name of the microorganism, and *L* is the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) between that full name and the user input. +#' - Use [mo_failures()] to get a [`vector`] with all values that could not be coerced to a valid value. +#' - Use [mo_renamed()] to get a [`data.frame`] with all values that could be coerced based on an old, previously accepted taxonomic name. #' -#' \strong{Microbial prevalence of pathogens in humans} \cr -#' The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \code{\link{microorganisms}} and \code{\link{microorganisms.old}} data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. +#' ## Microbial prevalence of pathogens in humans #' -#' 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 Enterobacteriales. +#' The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the `prevalence` columns in the [microorganisms] and [microorganisms.old] data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. #' -#' Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton} or \emph{Ureaplasma}. +#' 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 Enterobacteriales. +#' +#' Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Aspergillus*, *Bacteroides*, *Candida*, *Capnocytophaga*, *Chryseobacterium*, *Cryptococcus*, *Elisabethkingia*, *Flavobacterium*, *Fusobacterium*, *Giardia*, *Leptotrichia*, *Mycoplasma*, *Prevotella*, *Rhodotorula*, *Treponema*, *Trichophyton* or *Ureaplasma*. #' #' Group 3 (least prevalent microorganisms) consists of all other microorganisms. #' -#' \strong{Self-learning algorithm} \cr -#' The \code{as.mo()} function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use \code{clear_mo_history()} to reset the algorithms. Only experience from your current \code{AMR} package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge. +#' ## Self-learning algorithm +#' +#' The [as.mo()] function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use [clear_mo_history()] to reset the algorithms. Only experience from your current `AMR` package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge. #' -#' Usually, any guess after the first try runs 80-95\% faster than the first try. -#' -# \emph{For now, learning only works per session. If R is closed or terminated, the algorithms reset. This might be resolved in a future version.} -#' This resets with every update of this \code{AMR} package since results are saved to your local package library folder. +#' Usually, any guess after the first try runs 80-95% faster than the first try. +#' +#' This resets with every update of this `AMR` package since results are saved to your local package library folder. #' @inheritSection catalogue_of_life Catalogue of Life # (source as a section here, so it can be inherited by other man pages:) #' @section Source: -#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13} -#' -#' [2] 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. \url{https://doi.org/10.1016/j.cmi.2019.02.028} -#' -#' [3] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571} -#' -#' [4] 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}()}). +#' 1. Becker K *et al.* **Coagulase-Negative Staphylococci**. 2014. Clin Microbiol Rev. 27(4): 870–926. +#' 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. +#' 3. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95. +#' 4. Catalogue of Life: Annual Checklist (public online taxonomic database), (check included annual version with [catalogue_of_life_version()]). #' @export -#' @return Character (vector) with class \code{"mo"} -#' @seealso \code{\link{microorganisms}} for the \code{data.frame} that is being used to determine ID's. \cr -#' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code. +#' @return A [`character`] vector with class [`mo`] +#' @seealso [microorganisms] for the [`data.frame`] that is being used to determine ID's. +#' +#' The [mo_property()] functions (like [mo_genus()], [mo_gramstain()]) to get properties based on the returned code. #' @inheritSection AMR Read more on our website! #' @importFrom dplyr %>% pull left_join #' @examples diff --git a/R/mo_history.R b/R/mo_history.R index 13e3f0a15..d7f050e25 100644 --- a/R/mo_history.R +++ b/R/mo_history.R @@ -48,19 +48,6 @@ set_mo_history <- function(x, mo, uncertainty_level, force = FALSE, disable = FA if (NROW(mo_hist[base::which(mo_hist$x == x[i] & mo_hist$uncertainty_level >= uncertainty_level & mo_hist$package_version == utils::packageVersion("AMR")), ]) == 0) { - # # Not using the file system: - # tryCatch(options(mo_remembered_results = rbind(mo_hist, - # data.frame( - # x = x[i], - # mo = mo[i], - # uncertainty_level = uncertainty_level, - # package_version = base::as.character(utils::packageVersion("AMR")), - # stringsAsFactors = FALSE))), - # error = function(e) base::invisible()) - # # don't remember more than 1,000 different input values - # if (tryCatch(nrow(getOption("mo_remembered_results")), error = function(e) 1001) > 1000) { - # return(base::invisible()) - # } if (is.null(mo_hist) & interactive()) { warning_new_write <- TRUE } @@ -113,9 +100,6 @@ read_mo_history <- function(uncertainty_level = 2, force = FALSE, unfiltered = F } uncertainty_level_param <- uncertainty_level - # # Not using the file system: - # history <- tryCatch(getOption("mo_remembered_results"), - # error = function(e) NULL) history <- tryCatch(read.csv(mo_history_file(), stringsAsFactors = FALSE), warning = function(w) invisible(), error = function(e) NULL) @@ -159,9 +143,7 @@ clear_mo_history <- function(...) { return(invisible()) } } - # # Not using the file system: - # success <- tryCatch(options(mo_remembered_results = NULL), - # error = function(e) FALSE) + success <- create_blank_mo_history() if (!isFALSE(success)) { cat(red(paste("File", mo_history_file(), "cleared."))) diff --git a/R/mo_property.R b/R/mo_property.R index d6f29558a..3453a77a7 100755 --- a/R/mo_property.R +++ b/R/mo_property.R @@ -21,36 +21,33 @@ #' Property of a microorganism #' -#' Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with \code{\link{as.mo}}, which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples. -#' @param x any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.mo}} -#' @param property one of the column names of the \code{\link{microorganisms}} data set or \code{"shortname"} -#' @param language language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation. -#' @param ... other parameters passed on to \code{\link{as.mo}} -#' @param open browse the URL using \code{\link[utils]{browseURL}()} -#' @details All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for \code{mo_ref}, \code{mo_authors} and \code{mo_year}. This leads to the following results: -#' \itemize{ -#' \item{\code{mo_name("Chlamydia psittaci")} will return \code{"Chlamydophila psittaci"} (with a warning about the renaming)} -#' \item{\code{mo_ref("Chlamydia psittaci")} will return \code{"Page, 1968"} (with a warning about the renaming)} -#' \item{\code{mo_ref("Chlamydophila psittaci")} will return \code{"Everett et al., 1999"} (without a warning)} -#' } +#' Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with [as.mo()], which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples. +#' @param x any (vector of) text that can be coerced to a valid microorganism code with [as.mo()] +#' @param property one of the column names of the [microorganisms] data set or `"shortname"` +#' @param language language of the returned text, defaults to system language (see [get_locale()]) and can also be set with `getOption("AMR_locale")`. Use `language = NULL` or `language = ""` to prevent translation. +#' @param ... other parameters passed on to [as.mo()] +#' @param open browse the URL using [utils::browseURL()] +#' @details All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for [mo_ref()], [mo_authors()] and [mo_year()]. This leads to the following results: +#' - `mo_name("Chlamydia psittaci")` will return `"Chlamydophila psittaci"` (with a warning about the renaming) +#' - `mo_ref("Chlamydia psittaci")` will return `"Page, 1968"` (with a warning about the renaming) +#' - `mo_ref("Chlamydophila psittaci")` will return `"Everett et al., 1999"` (without a warning) #' -#' The Gram stain - \code{mo_gramstain()} - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value \code{NA}. +#' The Gram stain - [mo_gramstain()] - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value `NA`. #' -#' All output will be \link{translate}d where possible. +#' All output will be [translate]d where possible. #' -#' The function \code{mo_url()} will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. +#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. #' @inheritSection catalogue_of_life Catalogue of Life #' @inheritSection as.mo Source #' @rdname mo_property #' @name mo_property -#' @return \itemize{ -#' \item{An \code{integer} in case of \code{mo_year}} -#' \item{A \code{list} in case of \code{mo_taxonomy}} -#' \item{A named \code{character} in case of \code{mo_url}} -#' \item{A \code{character} in all other cases} -#' } +#' @return +#' - An [`integer`] in case of [mo_year()] +#' - A [`list`] in case of [mo_taxonomy()] +#' - A named [`character`] in case of [mo_url()] +#' - A [`character`] in all other cases #' @export -#' @seealso \code{\link{microorganisms}} +#' @seealso [microorganisms] #' @inheritSection AMR Read more on our website! #' @examples #' # taxonomic tree ----------------------------------------------------------- @@ -132,7 +129,7 @@ #' #' # get a list with the complete taxonomy (from kingdom to subspecies) #' mo_taxonomy("E. coli") -#' # get a list with the taxonomy, the authors and the URL to the online database +#' # get a list with the taxonomy, the authors, Gram-stain and URL to the online database #' mo_info("E. coli") #' } mo_name <- function(x, language = get_locale(), ...) { @@ -336,6 +333,7 @@ mo_info <- function(x, language = get_locale(), ...) { info <- lapply(x, function(y) c(mo_taxonomy(y, language = language), list(synonyms = mo_synonyms(y), + gramstain = mo_gramstain(y, language = language), url = unname(mo_url(y, open = FALSE)), ref = mo_ref(y)))) if (length(info) > 1) { diff --git a/R/mo_source.R b/R/mo_source.R index 96b75dfc5..2df960950 100644 --- a/R/mo_source.R +++ b/R/mo_source.R @@ -21,79 +21,81 @@ #' Use predefined reference data set #' -#' @description These functions can be used to predefine your own reference to be used in \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}. +#' @description These functions can be used to predefine your own reference to be used in [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()]. #' -#' This is \strong{the fastest way} to have your organisation (or analysis) specific codes picked up and translated by this package. +#' This is **the fastest way** to have your organisation (or analysis) specific codes picked up and translated by this package. #' @param path location of your reference file, see Details #' @rdname mo_source #' @name mo_source #' @aliases set_mo_source get_mo_source -#' @details The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the \code{readxl} package installed. +#' @details The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the `readxl` package installed. #' -#' \code{set_mo_source} will check the file for validity: it must be a \code{data.frame}, must have a column named \code{"mo"} which contains values from \code{microorganisms$mo} and must have a reference column with your own defined values. If all tests pass, \code{set_mo_source} will read the file into R and export it to \code{"~/.mo_source.rds"}. This compressed data file will then be used at default for MO determination (function \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}). The location of the original file will be saved as option with \code{\link{options}(mo_source = path)}. Its timestamp will be saved with \code{\link{options}(mo_source_datetime = ...)}. +#' [set_mo_source()] will check the file for validity: it must be a [`data.frame`], must have a column named `"mo"` which contains values from [`microorganisms$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into R and export it to `"~/.mo_source.rds"`. This compressed data file will then be used at default for MO determination (function [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()]). The location of the original file will be saved as option with `options(mo_source = path)`. Its timestamp will be saved with `options(mo_source_datetime = ...)`. #' -#' \code{get_mo_source} will return the data set by reading \code{"~/.mo_source.rds"} with \code{\link{readRDS}}. If the original file has changed (the file defined with \code{path}), it will call \code{set_mo_source} to update the data file automatically. +#' [get_mo_source()] will return the data set by reading `"~/.mo_source.rds"` with [readRDS()]. If the original file has changed (the file defined with `path`), it will call [set_mo_source()] to update the data file automatically. #' -#' Reading an Excel file (\code{.xlsx}) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by \code{get_mo_source} in only a couple of microseconds (a millionth of a second). -#' @section How it works: +#' Reading an Excel file (`.xlsx`) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by [get_mo_source()] in only a couple of microseconds (a millionth of a second). +#' +#' ## How it works +#' #' Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package: -#' \preformatted{ +#' ``` #' | A | B | #' --|--------------------|-------------| #' 1 | Organisation XYZ | mo | #' 2 | lab_mo_ecoli | B_ESCHR_COL | #' 3 | lab_mo_kpneumoniae | B_KLBSL_PNE | #' 4 | | | -#' } +#' ``` #' -#' We save it as \code{'home/me/ourcodes.xlsx'}. Now we have to set it as a source: -#' \preformatted{ +#' We save it as `"home/me/ourcodes.xlsx"`. Now we have to set it as a source: +#' ``` #' set_mo_source("home/me/ourcodes.xlsx") #' # Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'. -#' } +#' ``` #' -#' It has now created a file "~/.mo_source.rds" with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept. +#' It has now created a file `"~/.mo_source.rds"` with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept. #' #' And now we can use it in our functions: -#' \preformatted{ +#' ``` #' as.mo("lab_mo_ecoli") -#' [1] B_ESCHR_COL +#' \[1\] B_ESCHR_COLI #' #' mo_genus("lab_mo_kpneumoniae") #' [1] "Klebsiella" #' #' # other input values still work too #' as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli")) -#' [1] B_ESCHR_COL B_ESCHR_COL B_ESCHR_COL -#' } +#' [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI +#' ``` #' #' If we edit the Excel file to, let's say, this: -#' \preformatted{ -#' | A | B | -#' --|--------------------|-------------| -#' 1 | Organisation XYZ | mo | -#' 2 | lab_mo_ecoli | B_ESCHR_COL | -#' 3 | lab_mo_kpneumoniae | B_KLBSL_PNE | -#' 4 | lab_Staph_aureus | B_STPHY_AUR | -#' 5 | | | -#' } +#' ``` +#' | A | B | +#' --|--------------------|--------------| +#' 1 | Organisation XYZ | mo | +#' 2 | lab_mo_ecoli | B_ESCHR_COLI | +#' 3 | lab_mo_kpneumoniae | B_KLBSL_PNMN | +#' 4 | lab_Staph_aureus | B_STPHY_AURS | +#' 5 | | | +#' ``` #' #' ...any new usage of an MO function in this package will update your data: -#' \preformatted{ +#' ``` #' as.mo("lab_mo_ecoli") #' # Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'. -#' [1] B_ESCHR_COL +#' [1] B_ESCHR_COLI #' #' mo_genus("lab_Staph_aureus") #' [1] "Staphylococcus" -#' } +#' ``` #' #' To remove the reference completely, just use any of these: -#' \preformatted{ +#' ``` #' set_mo_source("") #' set_mo_source(NULL) #' # Removed mo_source file '~/.mo_source.rds'. -#' } +#' ``` #' @importFrom dplyr select everything #' @export #' @inheritSection AMR Read more on our website! diff --git a/R/p_symbol.R b/R/p_symbol.R index 79110bf5b..2eb26bbd1 100755 --- a/R/p_symbol.R +++ b/R/p_symbol.R @@ -19,11 +19,11 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' Symbol of a p value +#' Symbol of a p-value #' -#' Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}. +#' Return the symbol related to the p-value: 0 '`***`' 0.001 '`**`' 0.01 '`*`' 0.05 '`.`' 0.1 ' ' 1. Values above `p = 1` will return `NA`. #' @param p p value -#' @param emptychar text to show when \code{p > 0.1} +#' @param emptychar text to show when `p > 0.1` #' @return Text #' @inheritSection AMR Read more on our website! #' @export diff --git a/R/proportion.R b/R/proportion.R index 55cad8a80..895543610 100755 --- a/R/proportion.R +++ b/R/proportion.R @@ -21,31 +21,31 @@ #' Calculate microbial resistance #' -#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. +#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in [dplyr::summarise()] and support grouped variables, please see *Examples*. #' -#' \code{resistance()} should be used to calculate resistance, \code{susceptibility()} should be used to calculate susceptibility.\cr -#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples. -#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. -#' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}. -#' @param only_all_tested (for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below -#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}}) -#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}} +#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr +#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [as.rsi()] if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples. +#' @param minimum the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source. +#' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of `0.123456` will then be returned as `"12.3%"`. +#' @param only_all_tested (for combination therapies, i.e. using more than one variable for `...`): a logical to indicate that isolates must be tested for all antibiotics, see section *Combination therapy* below +#' @param data a [`data.frame`] containing columns with class [`rsi`] (see [as.rsi()]) +#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()] #' @inheritParams ab_property -#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}. -#' @param combine_IR a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}. +#' @param combine_SI a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter `combine_IR`, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is `TRUE`. +#' @param combine_IR a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter `combine_SI`. #' @inheritSection as.rsi Interpretation of S, I and R #' @details -#' The function \code{resistance()} is equal to the function \code{proportion_R()}. The function \code{susceptibility()} is equal to the function \code{proportion_SI()}. +#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()]. #' -#' \strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set. +#' **Remember that you should filter your table to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set. #' -#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{susceptibility()} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).} +#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can infuence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` parameter).* #' -#' The function \code{proportion_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and calculates the proportions R, I and S. The function \code{rsi_df()} works exactly like \code{proportion_df()}, but adds the number of isolates. +#' The function [proportion_df()] takes any variable from `data` that has an [`rsi`] class (created with [as.rsi()]) and calculates the proportions R, I and S. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates. #' @section Combination therapy: -#' When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI: +#' When using more than one variable for `...` (= combination therapy)), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how [susceptibility()] works to calculate the %SI: #' -#' \preformatted{ +#' ``` #' -------------------------------------------------------------------- #' only_all_tested = FALSE only_all_tested = TRUE #' ----------------------- ----------------------- @@ -62,23 +62,23 @@ #' R - - - - #' - - - - #' -------------------------------------------------------------------- -#' } +#' ``` #' -#' Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that: -#' \preformatted{ +#' Please note that, in combination therapies, for `only_all_tested = TRUE` applies that: +#' ``` #' count_S() + count_I() + count_R() = count_all() #' proportion_S() + proportion_I() + proportion_R() = 1 -#' } -#' and that, in combination therapies, for \code{only_all_tested = FALSE} applies that: -#' \preformatted{ +#' ``` +#' and that, in combination therapies, for `only_all_tested = FALSE` applies that: +#' ``` #' count_S() + count_I() + count_R() >= count_all() #' proportion_S() + proportion_I() + proportion_R() >= 1 -#' } +#' ``` #' -#' Using \code{only_all_tested} has no impact when only using one antibiotic as input. -#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. -#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptible isolates. -#' @return Double or, when \code{as_percent = TRUE}, a character. +#' Using `only_all_tested` has no impact when only using one antibiotic as input. +#' @source **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. . +#' @seealso [AMR::count()] to count resistant and susceptible isolates. +#' @return A [`double`] or, when `as_percent = TRUE`, a [`character`]. #' @rdname proportion #' @aliases portion #' @name proportion diff --git a/R/read.4d.R b/R/read.4d.R index 22802b650..4b982cfc7 100755 --- a/R/read.4d.R +++ b/R/read.4d.R @@ -18,12 +18,12 @@ # ANY WARRANTY OR LIABILITY. # # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # - +# #' Read data from 4D database #' -#' This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with \code{\link{as.mo}} and transform all antimicrobial columns with \code{\link{as.rsi}}. +#' This function is only useful for the MMB department of the UMCG. Use this function to **import data by just defining the `file` parameter**. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with [as.mo()] and transform all antimicrobial columns with [as.rsi()]. #' @inheritParams utils::read.table -#' @param info a logical to indicate whether info about the import should be printed, defaults to \code{TRUE} in interactive sessions +#' @param info a logical to indicate whether info about the import should be printed, defaults to `TRUE` in interactive sessions #' @details Column names will be transformed, but the original column names are set as a "label" attribute and can be seen in e.g. RStudio Viewer. #' @inheritSection AMR Read more on our website! #' @export @@ -84,7 +84,7 @@ read.4D <- function(file, colnames(data_4D) <- tolower(colnames(data_4D)) if (all(c("afnamedat", "gebdatum") %in% colnames(data_4D))) { - # add age + # add age column data_4D$age <- NA_integer_ } cols_wanted <- c("patientnr", "gebdatum", "age", "mv", "monsternr", "afnamedat", "bepaling", diff --git a/R/resistance_predict.R b/R/resistance_predict.R index a65deb834..21fd8a4b5 100755 --- a/R/resistance_predict.R +++ b/R/resistance_predict.R @@ -21,41 +21,40 @@ #' Predict antimicrobial resistance #' -#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example. -#' @param col_ab column name of \code{x} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}) +#' 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. +#' @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 \code{col_date} +#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date` #' @param year_max highest year to use in the prediction model, defaults to 10 years after today -#' @param year_every unit of sequence between lowest year found in the data and \code{year_max} +#' @param year_every unit of sequence between lowest year found in the data and `year_max` #' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model. -#' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options. -#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R}). The default, \code{TRUE}, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. -#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}. -#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model. +#' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options. +#' @param I_as_S a logical to indicate whether values `I` should be treated as `S` (will otherwise be treated as `R`). The default, `TRUE`, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section *Interpretation of S, I and R* below. +#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be `NA`. +#' @param info a logical to indicate whether textual analysis should be printed with the name and [summary()] of the statistical model. #' @param main title of the plot #' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars #' @param ... parameters passed on to functions #' @inheritSection as.rsi Interpretation of S, I and R #' @inheritParams first_isolate #' @inheritParams graphics::plot -#' @details Valid options for the statistical model are: -#' \itemize{ -#' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution} -#' \item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution} -#' \item{\code{"lin"} or \code{"linear"}: a linear regression model} -#' } -#' @return \code{data.frame} with extra class \code{"resistance_predict"} with columns: -#' \itemize{ -#' \item{\code{year}} -#' \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise} -#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)} -#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)} -#' \item{\code{observations}, the total number of available observations in that year, i.e. S + I + R} -#' \item{\code{observed}, the original observed resistant percentages} -#' \item{\code{estimated}, the estimated resistant percentages, calculated by the model} -#' } -#' Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples. -#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}} +#' @details Valid options for the statistical model (parameter `model`) are: +#' - `"binomial"` or `"binom"` or `"logit"`: a generalised linear regression model with binomial distribution +#' - `"loglin"` or `"poisson"`: a generalised log-linear regression model with poisson distribution +#' - `"lin"` or `"linear"`: a linear regression model +#' @return A [`data.frame`] with extra class [`resistance_predict`] with columns: +#' - `year` +#' - `value`, the same as `estimated` when `preserve_measurements = FALSE`, and a combination of `observed` and `estimated` otherwise +#' - `se_min`, the lower bound of the standard error with a minimum of `0` (so the standard error will never go below 0%) +#' - `se_max` the upper bound of the standard error with a maximum of `1` (so the standard error will never go above 100%) +#' - `observations`, the total number of available observations in that year, i.e. \eqn{S + I + R} +#' - `observed`, the original observed resistant percentages +#' - `estimated`, the estimated resistant percentages, calculated by the model +#' +#' Furthermore, the model itself is available as an attribute: `attributes(x)$model`, please see *Examples*. +#' @seealso The [proportion()] functions to calculate resistance +#' +#' Models: [lm()] [glm()] #' @rdname resistance_predict #' @export #' @importFrom stats predict glm lm @@ -63,7 +62,10 @@ #' @importFrom tidyr pivot_wider #' @inheritSection AMR Read more on our website! #' @examples -#' x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial") +#' x <- resistance_predict(example_isolates, +#' col_ab = "AMX", +#' year_min = 2010, +#' model = "binomial") #' plot(x) #' ggplot_rsi_predict(x) #' @@ -102,9 +104,9 @@ #' scale_y_continuous(limits = c(0, 1), #' breaks = seq(0, 1, 0.1), #' labels = paste0(seq(0, 100, 10), "%")) + -#' labs(title = expression(paste("Forecast of amoxicillin resistance in ", +#' labs(title = expression(paste("Forecast of Amoxicillin Resistance in ", #' italic("E. coli"))), -#' y = "%IR", +#' y = "%R", #' x = "Year") + #' theme_minimal(base_size = 13) #' } diff --git a/R/rsi.R b/R/rsi.R index 615028869..156fd5746 100755 --- a/R/rsi.R +++ b/R/rsi.R @@ -21,37 +21,35 @@ #' Class 'rsi' #' -#' Interpret MIC values according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning. +#' Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class [`rsi`], which is an ordered factor with levels `S < I < R`. Invalid antimicrobial interpretations will be translated as `NA` with a warning. #' @rdname as.rsi -#' @param x vector of values (for class \code{mic}: an MIC value in mg/L, for class \code{disk}: a disk diffusion radius in millimeters) -#' @param mo a microorganism code, generated with \code{\link{as.mo}} -#' @param ab an antimicrobial code, generated with \code{\link{as.ab}} +#' @param x vector of values (for class [`mic`]: an MIC value in mg/L, for class [`disk`]: a disk diffusion radius in millimeters) +#' @param mo a microorganism code, generated with [as.mo()] +#' @param ab an antimicrobial code, generated with [as.ab()] #' @inheritParams first_isolate -#' @param guideline defaults to the latest included EUCAST guideline, run \code{unique(AMR::rsi_translation$guideline)} for all options -#' @param threshold maximum fraction of invalid antimicrobial interpretations of \code{x}, see Examples +#' @param guideline defaults to the latest included EUCAST guideline, run `unique(AMR::rsi_translation$guideline)` for all options +#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, please see *Examples* #' @param ... parameters passed on to methods -#' @details Run \code{unique(AMR::rsi_translation$guideline)} for a list of all supported guidelines. +#' @details Run `unique(AMR::rsi_translation$guideline)` for a list of all supported guidelines. #' -#' After using \code{as.rsi}, you can use \code{\link{eucast_rules}} to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism. +#' After using [as.rsi()], you can use [eucast_rules()] to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism. #' -#' The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter. +#' The function [is.rsi.eligible()] returns `TRUE` when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` parameter. #' @section Interpretation of S, I and R: -#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations". +#' In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations". #' -#' \itemize{ -#' \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.} -#' \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.} -#' \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.} -#' } +#' - **S** - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent. +#' - **I** - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection. +#' - **R** - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure. #' #' Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection. #' -#' This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates. -#' @return Ordered factor with new class \code{rsi} +#' This AMR package honours this new insight. Use [susceptibility()] (equal to [proportion_SI()]) to determine antimicrobial susceptibility and [count_susceptible()] (equal to [count_SI()]) to count susceptible isolates. +#' @return Ordered factor with new class [`rsi`] #' @aliases RSI #' @export #' @importFrom dplyr %>% desc arrange filter -#' @seealso \code{\link{as.mic}} +#' @seealso [as.mic()] #' @inheritSection AMR Read more on our website! #' @examples #' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370))) diff --git a/R/rsi_df.R b/R/rsi_df.R index 8cae673a2..acf21e606 100644 --- a/R/rsi_df.R +++ b/R/rsi_df.R @@ -19,8 +19,7 @@ # Visit our website for more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # -#' @rdname proportion -#' @rdname count +#' @rdname proportion #' @export rsi_df <- function(data, translate_ab = "name", diff --git a/R/skewness.R b/R/skewness.R index bcd3522d0..bf72d3fd4 100755 --- a/R/skewness.R +++ b/R/skewness.R @@ -24,10 +24,10 @@ #' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. #' #' When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. 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zxlOa@O~$s3+ND=Uw~Q=kWw^?e!ecauK*C-GSf_x~S!Z2TJ6_0~V()$2_lze~_Qo`x#5HB@?V8LW1vjR+K@oCi-*cMM?_qmS9~f0Z zMyW6u6ea{bL|(gmXO2~R&mK9ds>l`Bda1TH)=!Q!lP>Q6dcYoSiN}OuRSI`ap7yT; zYFR}S3B_`<(p#6HvfM8hO$zg=&GFV!z;Es!*7aQie<%)8l%k0{O*8=}O^ar0*uN|< z0@3qLie>-;^%=TN-q*`#=1d@r!0bb>;6yr_WrZ}nE(7;$rRy2Kq%)s EwBj%*?EnA( diff --git a/R/translate.R b/R/translate.R index c4578d0bf..b8024610e 100755 --- a/R/translate.R +++ b/R/translate.R @@ -21,16 +21,16 @@ #' Translate strings from AMR package #' -#' For language-dependent output of AMR functions, like \code{\link{mo_name}}, \code{\link{mo_type}} and \code{\link{ab_name}}. -#' @details Strings will be translated to foreign languages if they are defined in a local translation file. Additions to this file can be suggested at our repository. The file can be found here: \url{https://gitlab.com/msberends/AMR/blob/master/data-raw/translations.tsv}. +#' For language-dependent output of AMR functions, like [mo_name()], [mo_type()] and [ab_name()]. +#' @details Strings will be translated to foreign languages if they are defined in a local translation file. Additions to this file can be suggested at our repository. The file can be found here: . #' -#' Currently supported languages can be found if running: \code{unique(AMR:::translations_file$lang)}. +#' Currently supported languages can be found if running: `unique(AMR:::translations_file$lang)`. #' -#' Please suggest your own translations \href{https://gitlab.com/msberends/AMR/issues/new?issue[title]=Translation\%20suggestion}{by creating a new issue on our repository}. +#' Please suggest your own translations [by creating a new issue on our repository](https://gitlab.com/msberends/AMR/issues/new?issue[title]=Translation\%20suggestion). #' -#' This file will be read by all functions where a translated output can be desired, like all \code{\link{mo_property}} functions (\code{\link{mo_fullname}}, \code{\link{mo_type}}, etc.). +#' This file will be read by all functions where a translated output can be desired, like all [mo_property()] functions ([mo_fullname()], [mo_type()], etc.). #' -#' The system language will be used at default, if that language is supported. The system language can be overwritten with \code{\link{getOption}("AMR_locale")}. +#' The system language will be used at default, if that language is supported. The system language can be overwritten with `Sys.setenv(AMR_locale = yourlanguage)`. #' @inheritSection AMR Read more on our website! #' @rdname translate #' @name translate diff --git a/R/whocc.R b/R/whocc.R index bddc5ef83..363f3e2b6 100755 --- a/R/whocc.R +++ b/R/whocc.R @@ -24,13 +24,13 @@ #' All antimicrobial drugs and their official names, ATC codes, ATC groups and defined daily dose (DDD) are included in this package, using the WHO Collaborating Centre for Drug Statistics Methodology. #' @section WHOCC: #' \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr} -#' This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). +#' This package contains **all ~550 antibiotic, antimycotic and antiviral drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, ) and the Pharmaceuticals Community Register of the European Commission (). #' #' These have become the gold standard for international drug utilisation monitoring and research. #' #' The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest. #' -#' \strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.} +#' **NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package.** See #' @inheritSection AMR Read more on our website! #' @name WHOCC #' @rdname WHOCC diff --git a/data-raw/eucast_rules.tsv b/data-raw/eucast_rules.tsv index 70110bb19..3e9a31401 100644 --- a/data-raw/eucast_rules.tsv +++ b/data-raw/eucast_rules.tsv @@ -1,6 +1,6 @@ # ------------------------------------------------------------------------------------------------------------------------------- # For editing this EUCAST reference file, these values can all be used for target antibiotics: -# 'all_betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_without_CAZ', +# 'all_betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_except_CAZ', # 'fluoroquinolones', 'glycopeptides', 'macrolides', 'polymyxins', 'streptogramins', 'tetracyclines', 'ureidopenicillins' # and all separate EARS-Net letter codes like 'AMC'. They can be separated by comma: 'AMC, fluoroquinolones'. # The 'if_mo_property' column can be any column name from the AMR::microorganisms data set, or "genus_species" or "gramstain". @@ -29,9 +29,9 @@ genus is Staphylococcus PEN, FOX S AMP, AMX, PIP, TIC S Staphylococcus Breakpoin genus is Staphylococcus PEN, FOX R, S OXA, FLC S Staphylococcus Breakpoints genus is Staphylococcus FOX R all_betalactams R Staphylococcus Breakpoints genus_species is Staphylococcus saprophyticus AMP S AMX, AMC, PIP, TZP S Staphylococcus Breakpoints -genus is Staphylococcus FOX S carbapenems, cephalosporins_without_CAZ S Staphylococcus Breakpoints -genus is Staphylococcus FOX I carbapenems, cephalosporins_without_CAZ I Staphylococcus Breakpoints -genus is Staphylococcus FOX R carbapenems, cephalosporins_without_CAZ R Staphylococcus Breakpoints +genus is Staphylococcus FOX S carbapenems, cephalosporins_except_CAZ S Staphylococcus Breakpoints +genus is Staphylococcus FOX I carbapenems, cephalosporins_except_CAZ I Staphylococcus Breakpoints +genus is Staphylococcus FOX R carbapenems, cephalosporins_except_CAZ R Staphylococcus Breakpoints genus is Staphylococcus NOR S CIP, LVX, MFX, OFX S Staphylococcus Breakpoints genus is Staphylococcus ERY S AZM, CLR, RXT S Staphylococcus Breakpoints genus is Staphylococcus ERY I AZM, CLR, RXT I Staphylococcus Breakpoints @@ -44,9 +44,9 @@ genus is Enterococcus AMP R AMX, AMC, PIP, TZP R Enterococcus Breakpoints genus is Enterococcus NOR S CIP, LVX S Enterococcus Breakpoints genus is Enterococcus NOR I CIP, LVX I Enterococcus Breakpoints genus is Enterococcus NOR R CIP, LVX R Enterococcus Breakpoints -genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN S aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC S Streptococcus groups A, B, C, G Breakpoints -genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN I aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC I Streptococcus groups A, B, C, G Breakpoints -genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN R aminopenicillins, ureidopenicillins, cephalosporins_without_CAZ, carbapenems, FLC, AMC R Streptococcus groups A, B, C, G Breakpoints +genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN S aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC S Streptococcus groups A, B, C, G Breakpoints +genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN I aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC I Streptococcus groups A, B, C, G Breakpoints +genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ PEN R aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC R Streptococcus groups A, B, C, G Breakpoints genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ NOR S LVX, MFX S Streptococcus groups A, B, C, G Breakpoints genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ ERY S AZM, CLR, RXT S Streptococcus groups A, B, C, G Breakpoints genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G)$ ERY I AZM, CLR, RXT I Streptococcus groups A, B, C, G Breakpoints @@ -171,17 +171,17 @@ genus_species is Staphylococcus haemolyticus CAZ R Table 04: Intrinsic resista genus_species is Staphylococcus intermedius CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus_species is Staphylococcus pseudintermedius CAZ R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus is Streptococcus FUS, CAZ, aminoglycosides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules -genus_species is Enterococcus faecalis FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules -genus_species is Enterococcus gallinarum FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules -genus_species is Enterococcus casseliflavus FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules -genus_species is Enterococcus faecium FUS, CAZ, cephalosporins_without_CAZ, aminoglycosides, macrolides, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules +genus_species is Enterococcus faecalis FUS, CAZ, cephalosporins_except_CAZ, aminoglycosides, macrolides, CLI, QDA, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules +genus_species is Enterococcus gallinarum FUS, CAZ, cephalosporins_except_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules +genus_species is Enterococcus casseliflavus FUS, CAZ, cephalosporins_except_CAZ, aminoglycosides, macrolides, CLI, QDA, VAN, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules +genus_species is Enterococcus faecium FUS, CAZ, cephalosporins_except_CAZ, aminoglycosides, macrolides, TMP, SXT R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus is Corynebacterium FOS R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus_species is Listeria monocytogenes cephalosporins R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus one_of Leuconostoc, Pediococcus glycopeptides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus is Lactobacillus glycopeptides R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus_species is Clostridium ramosum VAN R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules genus_species is Clostridium innocuum VAN R Table 04: Intrinsic resistance in Gram-positive bacteria Expert Rules -genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G) PEN S aminopenicillins, cephalosporins_without_CAZ, carbapenems S Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules +genus_species like ^Streptococcus (pyogenes|agalactiae|dysgalactiae|group A|group B|group C|group G) PEN S aminopenicillins, cephalosporins_except_CAZ, carbapenems S Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules genus is Enterococcus AMP R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules genus is Enterococcus AMX R ureidopenicillins, carbapenems R Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci Expert Rules family is Enterobacteriaceae TIC, PIP R, S PIP R Table 09: Interpretive rules for B-lactam agents and Gram-negative rods Expert Rules diff --git a/data-raw/internals.R b/data-raw/internals.R index 555e7dc94..65b158933 100644 --- a/data-raw/internals.R +++ b/data-raw/internals.R @@ -16,7 +16,7 @@ eucast_rules_file$reference.rule_group <- factor(eucast_rules_file$reference.rul eucast_rules_file <- dplyr::arrange(eucast_rules_file, reference.rule_group, reference.rule) - +eucast_rules_file$reference.rule_group <- as.character(eucast_rules_file$reference.rule_group) # Translations ---- translations_file <- utils::read.delim(file = "data-raw/translations.tsv", diff --git a/docs/404.html b/docs/404.html index a68daf9c6..2646ad931 100644 --- a/docs/404.html +++ b/docs/404.html @@ -84,7 +84,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index bf20ed9a8..bd1e94d46 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -84,7 +84,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 diff --git a/docs/articles/index.html b/docs/articles/index.html index 6d2ae8c9e..81de98ebb 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -84,7 +84,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 diff --git a/docs/authors.html b/docs/authors.html index 36da1e64c..b3988d085 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -84,7 +84,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 diff --git a/docs/index.html b/docs/index.html index 0f3e04605..6829cb28c 100644 --- a/docs/index.html +++ b/docs/index.html @@ -45,7 +45,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 diff --git a/docs/news/index.html b/docs/news/index.html index b285d27b0..b02bf0270 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -84,7 +84,7 @@ AMR (for R) - 0.8.0.9035 + 0.8.0.9036 @@ -231,13 +231,13 @@ -

+

-AMR 0.8.0.9035 Unreleased +AMR 0.8.0.9036 Unreleased

-
+

-Last updated: 24-Nov-2019 +Last updated: 28-Nov-2019

@@ -320,12 +320,15 @@
  • More robust way of determining valid MIC values
  • Small changed to the example_isolates data set to better reflect reality
  • Added more microorganisms codes from laboratory systems (esp. species of Pseudescherichia and Rodentibacter)
    • +
  • Added Gram-stain to mo_info() +

    • Other

      +
    • Rewrote the complete documentation to markdown format, to be able to use the very latest version of the great Roxygen2, released in November 2019. This tremously improved the documentation quality, since the rewrite forced us to go over all texts again and make changes where needed.
    • Change dependency on clean to cleaner, as this package was renamed accordingly upon CRAN request
    • Added Dr. Sofia Ny as contributor
    @@ -492,7 +495,7 @@ Since this is a major change, usage of the old also_single_tested w New
    • -

      Function rsi_df() to transform a data.frame 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 count_df() and portion_df() to immediately show resistance percentages and number of available isolates:

      +

      Function rsi_df() to transform a data.frame 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 count_df() and portion_df() to immediately show resistance percentages and number of available isolates:

      septic_patients %>%
   select(AMX, CIP) %>%
   rsi_df()
@@ -533,7 +536,7 @@ Since this is a major change, usage of the old also_single_tested w
 
      
 Changed
      
 
        
-
      • Column names of output count_df() and portion_df() are now lowercase
        • 
+
      • Column names of output count_df() and portion_df() are now lowercase
        • 
 
      • Fixed bug in translation of microorganism names
        • 
 
      • Fixed bug in determining taxonomic kingdoms
        • 
 
      • Algorithm improvements for as.ab() and as.mo() to understand even more severely misspelled input
        • 
@@ -611,7 +614,7 @@ Please guess_ab_col()        
 
 
      • Added ~5,000 more old taxonomic names to the microorganisms.old data set, which leads to better results finding when using the as.mo() function
        • 
-
      • This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as ‘increased exposure’ and not ‘intermediate’ anymore. For functions like portion_df() and count_df() this means that their new parameter combine_SI is TRUE at default. Our plotting function ggplot_rsi() also reflects this change since it uses count_df() internally.
        • 
+
      • This package now honours the new EUCAST insight (2019) that S and I are but classified as susceptible, where I is defined as ‘increased exposure’ and not ‘intermediate’ anymore. For functions like portion_df() and count_df() this means that their new parameter combine_SI is TRUE at default. Our plotting function ggplot_rsi() also reflects this change since it uses count_df() internally.
        • 
 
      • The age() function gained a new parameter exact to determine ages with decimals
        • 
 
      • Removed deprecated functions guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
 
        • 
@@ -1392,7 +1395,7 @@ Using as.mo(..., allow_uncertain = 3)
       
        Contents
        
       
          
-        
        • 0.8.0.9035
          • 
+        
        • 0.8.0.9036
          • 
         
        • 0.8.0
          • 
         
        • 0.7.1
          • 
         
        • 0.7.0
          • 
diff --git a/docs/reference/AMR-deprecated.html b/docs/reference/AMR-deprecated.html
index a8106de78..59c087b09 100644
--- a/docs/reference/AMR-deprecated.html
+++ b/docs/reference/AMR-deprecated.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
      @@ -247,7 +247,9 @@ portion_SI(...) -portion_S(...) +portion_S(...) + +portion_df(...)

      Read more on our website!

      diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html index f13f14dbc..4d6b4970a 100644 --- a/docs/reference/AMR.html +++ b/docs/reference/AMR.html @@ -85,7 +85,7 @@ AMR (for R) - 0.8.0.9032 + 0.8.0.9036
    @@ -244,7 +244,7 @@

    AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any table format, including WHONET/EARS-Net data.

    We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). 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 can be used for:

      -

    • Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life

      • +

    • Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life

    • Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines

    • Determining first isolates to be used for AMR analysis

    • Calculating antimicrobial resistance

      • @@ -268,11 +268,11 @@

      For suggestions, comments or questions, please contact us at:

      Matthijs S. Berends
      -m.s.berends [at] umcg [dot] nl
      +m.s.berends at umcg dot nl
      Department of Medical Microbiology, University of Groningen
      University Medical Center Groningen
      Post Office Box 30001
      -9700 RB Groningen +9700 RB Groningen
      The Netherlands

      If you have found a bug, please file a new issue at:
      https://gitlab.com/msberends/AMR/issues

      diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html index 330179f2b..ca04d429f 100644 --- a/docs/reference/WHOCC.html +++ b/docs/reference/WHOCC.html @@ -85,7 +85,7 @@ AMR (for R) - 0.8.0.9033 + 0.8.0.9036
    @@ -247,7 +247,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).

    These have become the gold standard for international drug utilisation monitoring and research.

    The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.

    -

    NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.

    +

    NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.

    Read more on our website!

    diff --git a/docs/reference/WHONET.html b/docs/reference/WHONET.html index 7ab33ea13..37be31721 100644 --- a/docs/reference/WHONET.html +++ b/docs/reference/WHONET.html @@ -85,7 +85,7 @@ AMR (for R) - 0.8.0.9032 + 0.8.0.9036
    @@ -234,7 +234,7 @@
    -

    This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our example_isolates data set.

    +

    This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our example_isolates data set.

    WHONET
    @@ -242,35 +242,34 @@

    Format

    -

    A data.frame with 500 observations and 53 variables:

    -
    Identification number

    ID of the sample

    -
    Specimen number

    ID of the specimen

    -
    Organism

    Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using as.mo.

    -
    Country

    Country of origin

    -
    Laboratory

    Name of laboratory

    -
    Last name

    Last name of patient

    -
    First name

    Initial of patient

    -
    Sex

    Gender of patient

    -
    Age

    Age of patient

    -
    Age category

    Age group, can also be looked up using age_groups

    -
    Date of admission

    Date of hospital admission

    -
    Specimen date

    Date when specimen was received at laboratory

    -
    Specimen type

    Specimen type or group

    -
    Specimen type (Numeric)

    Translation of "Specimen type"

    -
    Reason

    Reason of request with Differential Diagnosis

    -
    Isolate number

    ID of isolate

    -
    Organism type

    Type of microorganism, can also be looked up using mo_type

    -
    Serotype

    Serotype of microorganism

    -
    Beta-lactamase

    Microorganism produces beta-lactamase?

    -
    ESBL

    Microorganism produces extended spectrum beta-lactamase?

    -
    Carbapenemase

    Microorganism produces carbapenemase?

    -
    MRSA screening test

    Microorganism is possible MRSA?

    -
    Inducible clindamycin resistance

    Clindamycin can be induced?

    -
    Comment

    Other comments

    -
    Date of data entry

    Date this data was entered in WHONET

    -
    AMP_ND10:CIP_EE

    27 different antibiotics. You can lookup the abbreviatons in the antibiotics data set, or use e.g. ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.rsi.

    - -
    +

    A data.frame with 500 observations and 53 variables:

      +

    • Identification number
      ID of the sample

      • +

    • Specimen number
      ID of the specimen

      • +

    • Organism
      Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using as.mo().

      • +

    • Country
      Country of origin

      • +

    • Laboratory
      Name of laboratory

      • +

    • Last name
      Last name of patient

      • +

    • First name
      Initial of patient

      • +

    • Sex
      Gender of patient

      • +

    • Age
      Age of patient

      • +

    • Age category
      Age group, can also be looked up using age_groups()

      • +

    • Date of admission
      Date of hospital admission

      • +

    • Specimen date
      Date when specimen was received at laboratory

      • +

    • Specimen type
      Specimen type or group

      • +

    • Specimen type (Numeric)
      Translation of "Specimen type"

      • +

    • Reason
      Reason of request with Differential Diagnosis

      • +

    • Isolate number
      ID of isolate

      • +

    • Organism type
      Type of microorganism, can also be looked up using mo_type()

      • +

    • Serotype
      Serotype of microorganism

      • +

    • Beta-lactamase
      Microorganism produces beta-lactamase?

      • +

    • ESBL
      Microorganism produces extended spectrum beta-lactamase?

      • +

    • Carbapenemase
      Microorganism produces carbapenemase?

      • +

    • MRSA screening test
      Microorganism is possible MRSA?

      • +

    • Inducible clindamycin resistance
      Clindamycin can be induced?

      • +

    • Comment
      Other comments

      • +

    • Date of data entry
      Date this data was entered in WHONET

      • +

    • AMP_ND10:CIP_EE
      27 different antibiotics. You can lookup the abbreviatons in the antibiotics data set, or use e.g. ab_name("AMP") to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using as.rsi().

      • +

    Read more on our website!

    diff --git a/docs/reference/ab_property.html b/docs/reference/ab_property.html index 79bfa8b15..5683b164a 100644 --- a/docs/reference/ab_property.html +++ b/docs/reference/ab_property.html @@ -51,7 +51,7 @@ - + @@ -85,7 +85,7 @@ AMR (for R) - 0.8.0.9032 + 0.8.0.9036
    @@ -234,7 +234,7 @@
    -

    Use these functions to return a specific property of an antibiotic from the antibiotics data set. All input values will be evaluated internally with as.ab.

    +

    Use these functions to return a specific property of an antibiotic from the antibiotics data set. All input values will be evaluated internally with as.ab().

    ab_name(x, language = get_locale(), tolower = FALSE, ...)
@@ -264,11 +264,11 @@
     
     
       x
-      
    any (vector of) text that can be coerced to a valid microorganism code with as.ab
    
+      
    any (vector of) text that can be coerced to a valid microorganism code with as.ab()
    
     
     
       language
-      
    language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
    
+      
    language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
    
     
     
       tolower
@@ -276,7 +276,7 @@
     
     
       ...
-      
    other parameters passed on to as.ab
    
+      
    other parameters passed on to as.ab()
    
     
     
       administration
@@ -288,7 +288,7 @@
     
     
       property
-      
    one of the column names of one of the antibiotics data set
    
+      
    one of the column names of one of the antibiotics data set
    
     
     
 
@@ -296,10 +296,10 @@
 
     
 
      
-
    • An integer in case of ab_cid
      • 
-
    • A named list in case of ab_info and multiple ab_synonyms/ab_tradenames
      • 
-
    • A double in case of ab_ddd
      • 
-
    • A character in all other cases
      • 
+
    • An integer in case of ab_cid()
      • 
+
    • A named list in case of ab_info() and multiple ab_synonyms()/ab_tradenames()
      • 
+
    • A double in case of ab_ddd()
      • 
+
    • A character in all other cases
      • 
 
    
 
     
    Details
    
@@ -319,7 +319,7 @@
 
    On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
    
     
    See also
    
 
-    
    antibiotics
    
+    
    antibiotics
    
 
     
    Examples
    
     
    # all properties:
diff --git a/docs/reference/age.html b/docs/reference/age.html
index 9ea062995..caa74f6da 100644
--- a/docs/reference/age.html
+++ b/docs/reference/age.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -244,15 +244,15 @@
     
     
       x
-      
    date(s), will be coerced with as.POSIXlt
    
+      
    date(s), will be coerced with as.POSIXlt()
    
     
     
       reference
-      
    reference date(s) (defaults to today), will be coerced with as.POSIXlt and cannot be lower than x
    
+      
    reference date(s) (defaults to today), will be coerced with as.POSIXlt() and cannot be lower than x
    
     
     
       exact
-      
    a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of year-to-date (YTD) of x by the number of days in a year of reference (either 365 or 366).
    
+      
    a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of year-to-date (YTD) of x by the number of days in the year of reference (either 365 or 366).
    
     
     
       na.rm
@@ -270,7 +270,7 @@
 
    On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
    
     
    See also
    
 
-    
    To split ages into groups, use the age_groups function.
    
+    
    To split ages into groups, use the age_groups() function.
    
 
     
    Examples
    
     
    # 10 random birth dates
diff --git a/docs/reference/age_groups.html b/docs/reference/age_groups.html
index 77b2a19fd..54a07cab4 100644
--- a/docs/reference/age_groups.html
+++ b/docs/reference/age_groups.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -244,7 +244,7 @@
     
     
       x
-      
    age, e.g. calculated with age
    
+      
    age, e.g. calculated with age()
    
     
     
       split_at
@@ -263,7 +263,7 @@
 
     
    To split ages, the input can be:
      
 
    • A numeric vector. A vector of e.g. c(10, 20) will split on 0-9, 10-19 and 20+. A value of only 50 will split on 0-49 and 50+.
-        The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
      • 
+The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
      
 
    • A character:
        
 
      • "children" or "kids", equivalent of: c(0, 1, 2, 4, 6, 13, 18). This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.
        • 
 
      • "elderly" or "seniors", equivalent of: c(65, 75, 85). This will split on 0-64, 65-74, 75-84, 85+.
        • 
@@ -279,7 +279,7 @@
 
        On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
        
     
        See also
        
 
-    
        To determine ages, based on one or more reference dates, use the age function.
        
+    
        To determine ages, based on one or more reference dates, use the age() function.
        
 
     
        Examples
        
     
        ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
diff --git a/docs/reference/antibiotics.html b/docs/reference/antibiotics.html
index d2ffc7e3a..085317567 100644
--- a/docs/reference/antibiotics.html
+++ b/docs/reference/antibiotics.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9035
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
        
-    
        Two data sets containing all antibiotics/antimycotics and antivirals. Use as.ab or one of the ab_property functions to retrieve values from the antibiotics data set. Three identifiers are included in this data set: an antibiotic ID (ab, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (atc) as defined by the WHO, and a Compound ID (cid) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
        
+    
        Two data sets containing all antibiotics/antimycotics and antivirals. Use as.ab() or one of the ab_property() functions to retrieve values from the antibiotics data set. Three identifiers are included in this data set: an antibiotic ID (ab, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (atc) as defined by the WHO, and a Compound ID (cid) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
        
     
        
 
     
        antibiotics
@@ -244,35 +244,39 @@
 
     
        Format
        
 
-    
        For the antibiotics data set: a data.frame with 452 observations and 13 variables:
        
-  
        ab
        Antibiotic ID as used in this package (like AMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
        
-  
        atc
        ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like J01CR02
        
-  
        cid
        Compound ID as found in PubChem
        
-  
        name
        Official name as used by WHONET/EARS-Net or the WHO
        
-  
        group
        A short and concise group name, based on WHONET and WHOCC definitions
        
-  
        atc_group1
        Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like "Macrolides, lincosamides and streptogramins"
        
-  
        atc_group2
        Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like "Macrolides"
        
-  
        abbr
        List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)
        
-  
        synonyms
        Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
        
-  
        oral_ddd
        Defined Daily Dose (DDD), oral treatment
        
-  
        oral_units
        Units of oral_ddd
        
-  
        iv_ddd
        Defined Daily Dose (DDD), parenteral treatment
        
-  
        iv_units
        Units of iv_ddd
        
+    
        For the antibiotics data set: a data.frame with 452 observations and 13 variables:
        
 
-
        
+
          
+
        • ab
           Antibiotic ID as used in this package (like AMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
          • 
+
        • atc
           ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like J01CR02
          • 
+
        • cid
           Compound ID as found in PubChem
          • 
+
        • name
           Official name as used by WHONET/EARS-Net or the WHO
          • 
+
        • group
           A short and concise group name, based on WHONET and WHOCC definitions
          • 
+
        • atc_group1
           Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like "Macrolides, lincosamides and streptogramins"
          • 
+
        • atc_group2
           Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like "Macrolides"
          • 
+
        • abbr
           List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)
          • 
+
        • synonyms
           Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
          • 
+
        • oral_ddd
           Defined Daily Dose (DDD), oral treatment
          • 
+
        • oral_units
           Units of oral_ddd
          • 
+
        • iv_ddd
           Defined Daily Dose (DDD), parenteral treatment
          • 
+
        • iv_units
           Units of iv_ddd
          • 
+
        
 
-
        For the antivirals data set: a data.frame with 102 observations and 9 variables:
        
-  
        atc
        ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC
        
-  
        cid
        Compound ID as found in PubChem
        
-  
        name
        Official name as used by WHONET/EARS-Net or the WHO
        
-  
        atc_group
        Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
        
-  
        synonyms
        Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
        
-  
        oral_ddd
        Defined Daily Dose (DDD), oral treatment
        
-  
        oral_units
        Units of oral_ddd
        
-  
        iv_ddd
        Defined Daily Dose (DDD), parenteral treatment
        
-  
        iv_units
        Units of iv_ddd
        
 
-
        
+
        For the antivirals data set: a data.frame with 102 observations and 9 variables:
        
+
+
          
+
        • atc
           ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC
          • 
+
        • cid
           Compound ID as found in PubChem
          • 
+
        • name
           Official name as used by WHONET/EARS-Net or the WHO
          • 
+
        • atc_group
           Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
          • 
+
        • synonyms
           Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
          • 
+
        • oral_ddd
           Defined Daily Dose (DDD), oral treatment
          • 
+
        • oral_units
           Units of oral_ddd
          • 
+
        • iv_ddd
           Defined Daily Dose (DDD), parenteral treatment
          • 
+
        • iv_units
           Units of iv_ddd
          • 
+
        
+
     
        Source
        
 
     
        World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): https://www.whocc.no/atc_ddd_index/
        
@@ -290,7 +294,7 @@
 This package contains all ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
        
 
        These have become the gold standard for international drug utilisation monitoring and research.
        
 
        The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
        
-
        NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
        
+
        NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
        
     
        Read more on our website!
        
 
     
@@ -298,7 +302,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral dru
 
        On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
        
     
        See also
        
 
-    
        microorganisms
        
+    
        microorganisms
        
 
   
   
 
@@ -234,7 +234,7 @@
     
 
     
        
-    
        Use this function to determine the antibiotic code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and synonyms (brand names).
        
+    
        Use this function to determine the antibiotic code of one or more antibiotics. The data set antibiotics will be searched for abbreviations, official names and synonyms (brand names).
        
     
        
 
     
        as.ab(x, ...)
@@ -256,11 +256,11 @@
 
     
        Value
        
 
-    
        Character (vector) with class "ab". Unknown values will return NA.
        
+    
        Character (vector) with class ab. Unknown values will return NA.
        
     
        Details
        
 
-    
        All entries in the antibiotics data set have three different identifiers: a human readable EARS-Net code (column ab, used by ECDC and WHONET), an ATC code (column atc, used by WHO), and a CID code (column cid, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
        
-
        Use the ab_property functions to get properties based on the returned antibiotic ID, see Examples.
        
+    
        All entries in the antibiotics data set have three different identifiers: a human readable EARS-Net code (column ab, used by ECDC and WHONET), an ATC code (column atc, used by WHO), and a CID code (column cid, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
        
+
        Use the ab_property() functions to get properties based on the returned antibiotic ID, see Examples.
        
     
        Source
        
 
     
@@ -276,7 +276,7 @@
 This package contains all ~550 antibiotic, antimycotic and antiviral drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission (http://ec.europa.eu/health/documents/community-register/html/atc.htm).
        
 
        These have become the gold standard for international drug utilisation monitoring and research.
        
 
        The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
        
-
        NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
        
+
        NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.
        
     
        Read more on our website!
        
 
     
@@ -284,7 +284,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral dru
 
        On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
        
     
        See also
        
 
-    
        antibiotics for the dataframe that is being used to determine ATCs.
        
+    
        antibiotics for the dataframe that is being used to determine ATCs.
        
 
     
        Examples
        
     
        # These examples all return "ERY", the ID of Erythromycin:
diff --git a/docs/reference/as.disk.html b/docs/reference/as.disk.html
index 61fa87ea5..125956fda 100644
--- a/docs/reference/as.disk.html
+++ b/docs/reference/as.disk.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -259,7 +259,7 @@
     
        Ordered integer factor with new class disk
        
     
        Details
        
 
-    
        Interpret disk values as RSI values with as.rsi. It supports guidelines from EUCAST and CLSI.
        
+    
        Interpret disk values as RSI values with as.rsi(). It supports guidelines from EUCAST and CLSI.
        
     
        Read more on our website!
        
 
     
@@ -267,7 +267,7 @@
 
        On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
        
     
        See also
        
 
-    
        as.rsi
        
+    
        as.rsi()
        
 
     
        Examples
        
     
        # interpret disk values
diff --git a/docs/reference/as.mic.html b/docs/reference/as.mic.html
index 88811896f..fc94688b7 100644
--- a/docs/reference/as.mic.html
+++ b/docs/reference/as.mic.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
        
-    
        This transforms a vector to a new class mic, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as NA with a warning.
        
+    
        This transforms a vector to a new class mic, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as NA with a warning.
        
     
        
 
     
        as.mic(x, na.rm = FALSE)
@@ -256,10 +256,10 @@
 
     
        Value
        
 
-    
        Ordered factor with new class mic
        
+    
        Ordered factor with new class mic
        
     
        Details
        
 
-    
        Interpret MIC values as RSI values with as.rsi. It supports guidelines from EUCAST and CLSI.
        
+    
        To interpret MIC values as RSI values, use as.rsi() on MIC values. It supports guidelines from EUCAST and CLSI.
        
     
        Read more on our website!
        
 
     
@@ -267,7 +267,7 @@
 
        On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
        
     
        See also
        
 
-    
        as.rsi
        
+    
        as.rsi()
        
 
     
        Examples
        
     
        mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
diff --git a/docs/reference/as.mo.html b/docs/reference/as.mo.html
index bb52836f1..90e8042a0 100644
--- a/docs/reference/as.mo.html
+++ b/docs/reference/as.mo.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,11 +234,17 @@
     
 
     
        
-    
        Use this function to determine a valid microorganism ID (mo). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like "Staphylococcus aureus"), an abbreviated name (like "S. aureus"), an abbreviation known in the field (like "MRSA"), or just a genus. Please see Examples.
        
+    
        Use this function to determine a valid microorganism ID (mo). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like "Staphylococcus aureus"), an abbreviated name (like "S. aureus"), an abbreviation known in the field (like "MRSA"), or just a genus. Please see Examples.
        
     
        
 
-    
        as.mo(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE,
-  reference_df = get_mo_source(), ...)
+    
        as.mo(
+  x,
+  Becker = FALSE,
+  Lancefield = FALSE,
+  allow_uncertain = TRUE,
+  reference_df = get_mo_source(),
+  ...
+)
 
 is.mo(x)
 
@@ -255,25 +261,25 @@
     
     
       x
-      
        a character vector or a data.frame with one or two columns
        
+      
        a character vector or a data.frame with one or two columns
        
     
     
       Becker
-      
        a logical to indicate whether Staphylococci should be categorised into coagulase-negative Staphylococci ("CoNS") and coagulase-positive Staphylococci ("CoPS") instead of their own species, according to Karsten Becker et al. [1,2]. Note that this does not include species that were newly named after these publications, like S. caeli.
        
+      
        a logical to indicate whether Staphylococci should be categorised into coagulase-negative Staphylococci ("CoNS") and coagulase-positive Staphylococci ("CoPS") instead of their own species, according to Karsten Becker et al. (1,2). Note that this does not include species that were newly named after these publications, like S. caeli.
        
 
        This excludes Staphylococcus aureus at default, use Becker = "all" to also categorise S. aureus as "CoPS".
        
     
     
       Lancefield
-      
        a logical to indicate whether beta-haemolytic Streptococci should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [3]. These Streptococci will be categorised in their first group, e.g. Streptococcus dysgalactiae will be group C, although officially it was also categorised into groups G and L.
        
+      
        a logical to indicate whether beta-haemolytic Streptococci should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (3). These Streptococci will be categorised in their first group, e.g. Streptococcus dysgalactiae will be group C, although officially it was also categorised into groups G and L.
        
 
        This excludes Enterococci at default (who are in group D), use Lancefield = "all" to also categorise all Enterococci as group D.
        
     
     
       allow_uncertain
-      
        a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, see Details
        
+      
        a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, please see Details
        
     
     
       reference_df
-      
        a data.frame to use for extra reference when translating x to a valid mo. See set_mo_source and get_mo_source to automate the usage of your own codes (e.g. used in your analysis or organisation).
        
+      
        a data.frame to use for extra reference when translating x to a valid mo. See set_mo_source() and get_mo_source() to automate the usage of your own codes (e.g. used in your analysis or organisation).
        
     
     
       ...
@@ -283,12 +289,14 @@
 
     
        Value
        
 
-    
        Character (vector) with class "mo"
        
+    
        A character vector with class mo
        
     
        Details
        
 
-    
        General info 
        
-A microorganism ID from this package (class: mo) typically looks like these examples:
        -  Code               Full name
+    
+
        General info
        
+
+
+
        A microorganism ID from this package (class: mo) typically looks like these examples:
          Code               Full name
   ---------------    --------------------------------------
   B_KLBSL            Klebsiella
   B_KLBSL_PNMN       Klebsiella pneumoniae
@@ -303,26 +311,27 @@ A microorganism ID from this package (class: mo) typically looks li
 
        
 
 
        Values that cannot be coered will be considered 'unknown' and will get the MO code UNKNOWN.
        
-
        Use the mo_property_* functions to get properties based on the returned code, see Examples.
        
-
        The algorithm uses data from the Catalogue of Life (see below) and from one other source (see microorganisms).
        
-
        The as.mo() function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
          
+
          Use the mo_property_* functions to get properties based on the returned code, see Examples.
          
+
          The algorithm uses data from the Catalogue of Life (see below) and from one other source (see microorganisms).
          
+
          The as.mo() function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
            
 
          1. Human pathogenic prevalence: the function  starts with more prevalent microorganisms, followed by less prevalent ones;
            2. 
 
          2. Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;
            3. 
 
          3. Breakdown of input values to identify possible matches.
            4. 
-
        
+
 
 
        This will lead to the effect that e.g. "E. coli" (a highly prevalent microorganism found in humans) will return the microbial ID of Escherichia coli and not Entamoeba coli (a less prevalent microorganism in humans), although the latter would alphabetically come first.
        
-
        Coping with uncertain results 
        
-In addition, the as.mo() function can differentiate four levels of uncertainty to guess valid results:
        
-
          
+
+
          Coping with uncertain results
          
+
+
+
          In addition, the as.mo() function can differentiate four levels of uncertainty to guess valid results:
            
 
          • Uncertainty level 0: no additional rules are applied;
            • 
 
          • Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;
            • 
 
          • Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
            • 
 
          • Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.
            • 
 
          
 
-
          This leads to e.g.:
          
-
            
+
            This leads to e.g.:
              
 
            • "Streptococcus group B (known as S. agalactiae)". The text between brackets will be removed and a warning will be thrown that the result Streptococcus group B (B_STRPT_GRPB) needs review.
              • 
 
            • "S. aureus - please mind: MRSA". The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result Staphylococcus aureus (B_STPHY_AURS) needs review.
              • 
 
            • "Fluoroquinolone-resistant Neisseria gonorrhoeae". The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result Neisseria gonorrhoeae (B_NESSR_GNRR) needs review.
              • 
@@ -330,35 +339,45 @@ In addition, the as.mo() function can differentiate four levels of
 
 
              The level of uncertainty can be set using the argument allow_uncertain. The default is allow_uncertain = TRUE, which is equal to uncertainty level 2. Using allow_uncertain = FALSE is equal to uncertainty level 0 and will skip all rules. You can also use e.g. as.mo(..., allow_uncertain = 1) to only allow up to level 1 uncertainty.
              
 
              There are three helper functions that can be run after then as.mo() function:
                
-
              • Use mo_uncertainties() to get a data.frame with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as (n - 0.5 * L) / n, where n is the number of characters of the returned full name of the microorganism, and L is the Levenshtein distance between that full name and the user input.
                • 
-
              • Use mo_failures() to get a vector with all values that could not be coerced to a valid value.
                • 
-
              • Use mo_renamed() to get a data.frame with all values that could be coerced based on an old, previously accepted taxonomic name.
                • 
-
                 
+
            • Use mo_uncertainties() to get a data.frame with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \((n - 0.5 * L) / n\), where n is the number of characters of the returned full name of the microorganism, and L is the Levenshtein distance between that full name and the user input.
              • 
+
            • Use mo_failures() to get a vector with all values that could not be coerced to a valid value.
              • 
+
            • Use mo_renamed() to get a data.frame with all values that could be coerced based on an old, previously accepted taxonomic name.
              • 
+
            
 
-
            Microbial prevalence of pathogens in humans 
            
-The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the prevalence columns in the microorganisms and microorganisms.old data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.
            
+
+
            Microbial prevalence of pathogens in humans
            
+
+
+
            The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the prevalence columns in the microorganisms and microorganisms.old data sets. The grouping into prevalence groups 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 Enterobacteriales.
            
 
            Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is Aspergillus, Bacteroides, Candida, Capnocytophaga, Chryseobacterium, Cryptococcus, Elisabethkingia, Flavobacterium, Fusobacterium, Giardia, Leptotrichia, Mycoplasma, Prevotella, Rhodotorula, Treponema, Trichophyton or Ureaplasma.
            
 
            Group 3 (least prevalent microorganisms) consists of all other microorganisms.
            
-
            Self-learning algorithm 
            
-The as.mo() function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use clear_mo_history() to reset the algorithms. Only experience from your current AMR package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge.
            
+
+
            Self-learning algorithm
            
+
+
+
            The as.mo() function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use clear_mo_history() to reset the algorithms. Only experience from your current AMR package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge.
            
 
            Usually, any guess after the first try runs 80-95% faster than the first try.
            
 
            This resets with every update of this AMR package since results are saved to your local package library folder.
            
+
     
            Source
            
 
     
 
-
            [1] Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13
            
-
            [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. https://doi.org/10.1016/j.cmi.2019.02.028
            
-
            [3] Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
            
-
            [4] Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
            
+
              
+
            1. Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13
              2. 
+
            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. https://doi.org/10.1016/j.cmi.2019.02.028
              3. 
+
            3. Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
              4. 
+
            4. Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
              5. 
+
            
+
     
            Catalogue of Life
            
 
     
 
 
             
            
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
            
-
            Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
            
+
            Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
            
     
            Read more on our website!
            
 
     
@@ -366,8 +385,8 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
            On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
            
     
            See also
            
 
-    
            microorganisms for the data.frame that is being used to determine ID's. 
            
-The mo_property functions (like mo_genus, mo_gramstain) to get properties based on the returned code.
            
+    
            microorganisms for the data.frame that is being used to determine ID's.
            
+
            The mo_property() functions (like mo_genus(), mo_gramstain()) to get properties based on the returned code.
            
 
     
            Examples
            
     
            # \donttest{
diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html
index 52caf178d..01ebdf8b5 100644
--- a/docs/reference/as.rsi.html
+++ b/docs/reference/as.rsi.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
            
-    
            Interpret MIC values according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class rsi, which is an ordered factor with levels S < I < R. Invalid antimicrobial interpretations will be translated as NA with a warning.
            
+    
            Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class rsi, which is an ordered factor with levels S < I < R. Invalid antimicrobial interpretations will be translated as NA with a warning.
            
     
            
 
     
            as.rsi(x, ...)
@@ -246,8 +246,7 @@
 as.rsi(x, mo, ab, guideline = "EUCAST", ...)
 
 # S3 method for data.frame
-as.rsi(x, col_mo = NULL, guideline = "EUCAST",
-  ...)
+as.rsi(x, col_mo = NULL, guideline = "EUCAST", ...)
 
 is.rsi(x)
 
@@ -258,7 +257,7 @@
     
     
       x
-      
            vector of values (for class mic: an MIC value in mg/L, for class disk: a disk diffusion radius in millimeters)
            
+      
            vector of values (for class mic: an MIC value in mg/L, for class disk: a disk diffusion radius in millimeters)
            
     
     
       ...
@@ -266,11 +265,11 @@
     
     
       mo
-      
            a microorganism code, generated with as.mo
            
+      
            a microorganism code, generated with as.mo()
            
     
     
       ab
-      
            an antimicrobial code, generated with as.ab
            
+      
            an antimicrobial code, generated with as.ab()
            
     
     
       guideline
@@ -278,11 +277,11 @@
     
     
       col_mo
-      
            column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
            
+      
            column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
            
     
     
       threshold
-      
            maximum fraction of invalid antimicrobial interpretations of x, see Examples
            
+      
            maximum fraction of invalid antimicrobial interpretations of x, please see Examples
            
     
     
 
@@ -292,21 +291,20 @@
     
            Details
            
 
     
            Run unique(AMR::rsi_translation$guideline) for a list of all supported guidelines.
            
-
            After using as.rsi, you can use eucast_rules to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
            
-
            The function is.rsi.eligible returns TRUE when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE otherwise. The threshold of 5% can be set with the threshold parameter.
            
+
            After using as.rsi(), you can use eucast_rules() to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
            
+
            The function is.rsi.eligible() returns TRUE when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE otherwise. The threshold of 5% can be set with the threshold parameter.
            
     
            Interpretation of S, I and R
            
 
     
 
-
            In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
            
-
              
+
              In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                
 
              • S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
                • 
 
              • I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
                • 
 
              • R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
                • 
 
              
 
 
              Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
              
-
              This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
              
+
              This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
              
     
              Read more on our website!
              
 
     
@@ -314,7 +312,7 @@
 
              On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
              
     
              See also
              
 
-    
              as.mic
              
+    
              as.mic()
              
 
     
              Examples
              
     
              rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html
index 730425d6d..c6c633525 100644
--- a/docs/reference/atc_online.html
+++ b/docs/reference/atc_online.html
@@ -51,7 +51,8 @@
   
 
 
-
+
 
 
 
@@ -85,7 +86,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,11 +235,16 @@
     
 
     
              
-    
              Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. 
               This function requires an internet connection.
              
+    
              Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit.
              
+
              This function requires an internet connection.
              
     
              
 
-    
              atc_online_property(atc_code, property, administration = "O",
-  url = "https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no")
+    
              atc_online_property(
+  atc_code,
+  property,
+  administration = "O",
+  url = "https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no"
+)
 
 atc_online_groups(atc_code, ...)
 
@@ -290,7 +296,7 @@
 
              Abbreviations of return values when using property = "U" (unit):
                
 
              • "g" = gram
                • 
 
              • "mg" = milligram
                • 
-
              • "mcg" = microgram
                • 
+
              • `"mcg"`` = microgram
                • 
 
              • "U" = unit
                • 
 
              • "TU" = thousand units
                • 
 
              • "MU" = million units
                • 
diff --git a/docs/reference/availability.html b/docs/reference/availability.html
index 6d1646ccb..fc0c2082b 100644
--- a/docs/reference/availability.html
+++ b/docs/reference/availability.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
                
-    
                Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. resistance.
                
+    
                Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. resistance().
                
     
                
 
     
                availability(tbl, width = NULL)
                
@@ -244,7 +244,7 @@
     
     
       tbl
-      
                a data.frame or list
                
+      
                a data.frame or list
                
     
     
       width
@@ -254,10 +254,10 @@
 
     
                Value
                
 
-    
                data.frame with column names of tbl as row names
                
+    
                data.frame with column names of tbl as row names
                
     
                Details
                
 
-    
                The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with resistance.
                
+    
                The function returns a data.frame with columns "resistant" and "visual_resistance". The values in that columns are calculated with resistance().
                
     
                Read more on our website!
                
 
     
diff --git a/docs/reference/bug_drug_combinations.html b/docs/reference/bug_drug_combinations.html
index 274b406a4..cb62482f4 100644
--- a/docs/reference/bug_drug_combinations.html
+++ b/docs/reference/bug_drug_combinations.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,18 +234,25 @@
     
 
     
                
-    
                Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use format on the result to prettify it to a publicable/printable format, see Examples.
                
+    
                Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use format() on the result to prettify it to a publicable/printable format, see Examples.
                
     
                
 
     
                bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
 
 # S3 method for bug_drug_combinations
-format(x,
-  translate_ab = "name (ab, atc)", language = get_locale(),
-  minimum = 30, combine_SI = TRUE, combine_IR = FALSE,
-  add_ab_group = TRUE, remove_intrinsic_resistant = FALSE,
-  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark ==
-  ",", ".", ","), ...)
                
+format(
+  x,
+  translate_ab = "name (ab, atc)",
+  language = get_locale(),
+  minimum = 30,
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  add_ab_group = TRUE,
+  remove_intrinsic_resistant = FALSE,
+  decimal.mark = getOption("OutDec"),
+  big.mark = ifelse(decimal.mark == ",", ".", ","),
+  ...
+)
              
 
     
              Arguments
              
     
@@ -256,11 +263,11 @@
     
-      
+      
-      
+      
@@ -268,11 +275,11 @@
     
-      
+      
-      
+      
@@ -312,10 +319,10 @@
     
              M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
              Value
              
 
-    
              The function bug_drug_combinations returns a data.frame with columns "mo", "ab", "S", "I", "R" and "total".
              
+    
              The function bug_drug_combinations() returns a data.frame with columns "mo", "ab", "S", "I", "R" and "total".
              Details
              
 
-    
              The function format calculates the resistance per bug-drug combination. Use combine_IR = FALSE (default) to test R vs. S+I and combine_IR = TRUE to test R+I vs. S.
              
+    
              The function format() calculates the resistance per bug-drug combination. Use combine_IR = FALSE (default) to test R vs. S+I and combine_IR = TRUE to test R+I vs. S.
              The language of the output can be overwritten with options(AMR_locale), please see translate.
              Read more on our website!
              
 
diff --git a/docs/reference/catalogue_of_life.html b/docs/reference/catalogue_of_life.html
index 08e5ff570..2d1a97ca8 100644
--- a/docs/reference/catalogue_of_life.html
+++ b/docs/reference/catalogue_of_life.html
@@ -85,7 +85,7 @@
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
 
@@ -245,7 +245,7 @@
 
 
               
              
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
              
-
              Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
              
+
              Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
              Included taxa
              
 
     
@@ -268,8 +268,8 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
              On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
              See also
              
 
-    
              Data set microorganisms for the actual data. 
              
-Function as.mo() to use the data for intelligent determination of microorganisms.
              
+    
              Data set microorganisms for the actual data. 
              
+Function as.mo() to use the data for intelligent determination of microorganisms.
              Examples
              # Get version info of included data set
@@ -295,7 +295,7 @@ Function as.mo() to use the data for intel
 mo_ref("E. coli")
 # [1] "Castellani et al., 1919"
 
-# Do not get mistaken - the package only includes microorganisms
+# Do not get mistaken - this package is about microorganisms
 mo_kingdom("C. elegans")
 # [1] "Bacteria"                        # Bacteria?!
 mo_name("C. elegans")
diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html
index 77ceae90d..82c8f543d 100644
--- a/docs/reference/catalogue_of_life_version.html
+++ b/docs/reference/catalogue_of_life_version.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -242,10 +242,10 @@
 
     
              Value
              
 
-    
              a list, which prints in pretty format
              
+    
              a list, which prints in pretty format
              
     
              Details
              
 
-    
              For DSMZ, see ?microorganisms.
              
+    
              For DSMZ, see microorganisms.
              
     
              Catalogue of Life
              
 
     
@@ -260,7 +260,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
              On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
              
     
              See also
              
 
-    
              microorganisms
              
+    
              microorganisms
              
 
     
              Examples
              
     
              library(dplyr)
diff --git a/docs/reference/count.html b/docs/reference/count.html
index e342195ef..22307f5ac 100644
--- a/docs/reference/count.html
+++ b/docs/reference/count.html
@@ -6,7 +6,7 @@
 
 
 
-Count isolates — count • AMR (for R)
+Count available isolates — count • AMR (for R)
 
 
 
@@ -50,8 +50,8 @@
   
   
 
-
-
+
 
 
@@ -86,7 +86,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -229,14 +229,14 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
              
   
              
     
              
-    
              Count isolates
              
+    
              Count available isolates
              
     
     
     
              
 
     
              
-    
              These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
              
-
              count_resistant() should be used to count resistant isolates, count_susceptible() should be used to count susceptible isolates.
              
+    
              These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in summarise() and support grouped variables, see Examples.
              
+
              count_resistant() should be used to count resistant isolates, count_susceptible() should be used to count susceptible isolates.
              
     
              
 
     
              count_resistant(..., only_all_tested = FALSE)
@@ -257,31 +257,36 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
 n_rsi(..., only_all_tested = FALSE)
 
-count_df(data, translate_ab = "name", language = get_locale(),
-  combine_SI = TRUE, combine_IR = FALSE)
              
+count_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
              
 
     
              Arguments
              
     
              
     
              
       col_mo
              column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
              column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
              		
     
              
     
              
       FUN
              the function to call on the mo column to transform the microorganism IDs, defaults to mo_shortname
              the function to call on the mo column to transform the microorganism IDs, defaults to mo_shortname()
              		
     
              
     
              
       ...
              
     
              
       translate_ab
              a character of length 1 containing column names of the antibiotics data set
              a character of length 1 containing column names of the antibiotics data set
              		
     
              
     
              
       language
              language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
              language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
              		
     
              
     
              
       minimum
     
     
 
     
       
     
     
     
 
     
     
              
-      
+      
-      
+      
-      
+      
-      
+      
-      
+      
@@ -295,33 +300,30 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
     
              Value
              
 
-    
              Integer
              
+    
              An integer
              Details
              
 
-    
              These functions are meant to count isolates. Use the resistance/susceptibility functions to calculate microbial resistance/susceptibility.
              
+    
              These functions are meant to count isolates. Use the resistance()/susceptibility() functions to calculate microbial resistance/susceptibility.
              The function count_resistant() is equal to the function count_R(). The function count_susceptible() is equal to the function count_SI().
              
-
              The function n_rsi() is an alias of count_all(). They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct(). Their function is equal to count_susceptible(...) + count_resistant(...).
              
-
              The function count_df() takes any variable from data that has an "rsi" class (created with as.rsi()) and counts the number of S's, I's and R's. The function rsi_df() works exactly like count_df(), but adds the percentage of S, I and R.
              
+
              The function n_rsi() is an alias of count_all(). They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct(). Their function is equal to count_susceptible(...) + count_resistant(...).
              
+
              The function count_df() takes any variable from data that has an rsi class (created with as.rsi()) and counts the number of S's, I's and R's. The function rsi_df() works exactly like count_df(), but adds the percentage of S, I and R.
              Interpretation of S, I and R
              
 
     
 
-
              In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
              
-
                
+
                In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                  
 
                • S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
                  • 
 
                • I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
                  • 
 
                • R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
                  • 
 
                
 
 
                Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
                
-
                This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                
+
                This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                
     
                Combination therapy
                
 
     
 
-
                When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how susceptibility works to calculate the %SI:
                
-
                ---------------------------------------------------------------------
+
                When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how susceptibility() works to calculate the %SI:
                --------------------------------------------------------------------
                     only_all_tested = FALSE  only_all_tested = TRUE
                     -----------------------  -----------------------
  Drug A    Drug B   include as  include as   include as  include as
@@ -339,11 +341,11 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 --------------------------------------------------------------------
 
                
 
-
                Please note that, in combination therapies, for only_all_tested = TRUE applies that:
                -    count_S()    +   count_I()    +   count_R()    = count_all()
+
                Please note that, in combination therapies, for only_all_tested = TRUE applies that:
                    count_S()    +   count_I()    +   count_R()    = count_all()
   proportion_S() + proportion_I() + proportion_R() = 1
-
                and that, in combination therapies, for only_all_tested = FALSE applies that:
                -    count_S()    +   count_I()    +   count_R()    >= count_all()
+
                
+
+
                and that, in combination therapies, for only_all_tested = FALSE applies that:
                    count_S()    +   count_I()    +   count_R()    >= count_all()
   proportion_S() + proportion_I() + proportion_R() >= 1
 
                
 
@@ -355,7 +357,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
                On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                
     
                See also
                
 
-    
                proportion_* to calculate microbial resistance and susceptibility.
                
+    
                proportion_* to calculate microbial resistance and susceptibility.
                
 
     
                Examples
                
     
                # example_isolates is a data set available in the AMR package.
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html
index 48fafcd24..f4a2dbfff 100644
--- a/docs/reference/eucast_rules.html
+++ b/docs/reference/eucast_rules.html
@@ -86,7 +86,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
       
       
         AMR (for R)
-        0.8.0.9033
+        0.8.0.9036
       
     
 
@@ -239,8 +239,14 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
 
                To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules are applied at default, see Details.
                
     
 
-    
                eucast_rules(x, col_mo = NULL, info = TRUE, rules = c("breakpoints",
-  "expert", "other", "all"), verbose = FALSE, ...)
                
+    
                eucast_rules(
+  x,
+  col_mo = NULL,
+  info = TRUE,
+  rules = c("breakpoints", "expert", "other", "all"),
+  verbose = FALSE,
+  ...
+)
                
 
     
                Arguments
                
     
              
     
     
              
       ...
              one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed.
              one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi() if needed.
              		
     
              
     
              
       only_all_tested
              (for combination therapies, i.e. using more than one variable for ...) a logical to indicate that isolates must be tested for all antibiotics, see section Combination therapy below
              (for combination therapies, i.e. using more than one variable for ...): a logical to indicate that isolates must be tested for all antibiotics, see section Combination therapy below
              		
     
              
     
              
       data
              a data.frame containing columns with class rsi (see as.rsi)
              a data.frame containing columns with class rsi (see as.rsi())
              		
     
              
     
              
       translate_ab
              a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property
              a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property()
              		
     
              
     
              
       language
              language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
              language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
              		
     
              
     
              
       combine_SI
     
 
     
              
@@ -251,7 +257,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
-      
+      
@@ -267,7 +273,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
-      
+      
              
     
              
       col_mo
              column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
              column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
              		
     
              
     
              
       info
              
     
              
       ...
              column name of an antibiotic, see section Antibiotics
              column name of an antibiotic, please see section Antibiotics below
              		
     
              
     
              
 
@@ -276,20 +282,20 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
 
 
     
              Value
              
 
-    
              The input of x, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
              
+    
              The input of x, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
              
     
              Details
              
 
-    
              Note: This function does not translate MIC values to RSI values. Use as.rsi for that. 
              
+    
              Note: This function does not translate MIC values to RSI values. Use as.rsi() for that. 
              
 Note: When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
              
 
              Before further processing, some non-EUCAST rules are applied to improve the efficacy of the EUCAST rules. These non-EUCAST rules, that are applied to all isolates, are:
                
 
              • Inherit amoxicillin (AMX) from ampicillin (AMP), where amoxicillin (AMX) is unavailable;
                • 
@@ -299,94 +305,96 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
 
              • Set trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R;
                • 
 
              • Set amoxicillin/clavulanic acid (AMC) = S where amoxicillin (AMX) = S;
                • 
 
              • Set piperacillin/tazobactam (TZP) = S where piperacillin (PIP) = S;
                • 
-
              • Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S.
                • 
-
              To not use these rules, please use eucast_rules(..., rules = c("breakpoints", "expert")).
              
+
            • Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S.
+To not use these rules, please use eucast_rules(..., rules = c("breakpoints", "expert")).
              • 
+
            
+
 
            The file containing all EUCAST rules is located here: https://gitlab.com/msberends/AMR/blob/master/data-raw/eucast_rules.tsv.
            
     
            Antibiotics
            
 
     
 
-
            To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
            
-
            The following antibiotics are used for the functions eucast_rules and mdro. These are shown below in the format 'antimicrobial ID: name (ATC code)', sorted by name:
            
+
            To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col() or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
            
+
            The following antibiotics are used for the functions eucast_rules() and mdro(). These are shown below in the format 'antimicrobial ID: name (ATC code)', sorted by name:
            
 
            AMK: amikacin (J01GB06),
- AMX: amoxicillin (J01CA04),
- AMC: amoxicillin/clavulanic acid (J01CR02),
- AMP: ampicillin (J01CA01),
- SAM: ampicillin/sulbactam (J01CR01),
- AZM: azithromycin (J01FA10),
- AZL: azlocillin (J01CA09),
- ATM: aztreonam (J01DF01),
- CAP: capreomycin (J04AB30),
- RID: cefaloridine (J01DB02),
- CZO: cefazolin (J01DB04),
- FEP: cefepime (J01DE01),
- CTX: cefotaxime (J01DD01),
- CTT: cefotetan (J01DC05),
- FOX: cefoxitin (J01DC01),
- CPT: ceftaroline (J01DI02),
- CAZ: ceftazidime (J01DD02),
- CRO: ceftriaxone (J01DD04),
- CXM: cefuroxime (J01DC02),
- CED: cephradine (J01DB09),
- CHL: chloramphenicol (J01BA01),
- CIP: ciprofloxacin (J01MA02),
- CLR: clarithromycin (J01FA09),
- CLI: clindamycin (J01FF01),
- COL: colistin (J01XB01),
- DAP: daptomycin (J01XX09),
- DOR: doripenem (J01DH04),
- DOX: doxycycline (J01AA02),
- ETP: ertapenem (J01DH03),
- ERY: erythromycin (J01FA01),
- ETH: ethambutol (J04AK02),
- FLC: flucloxacillin (J01CF05),
- FOS: fosfomycin (J01XX01),
- FUS: fusidic acid (J01XC01),
- GAT: gatifloxacin (J01MA16),
- GEN: gentamicin (J01GB03),
- GEH: gentamicin-high (no ATC code),
- IPM: imipenem (J01DH51),
- INH: isoniazid (J04AC01),
- KAN: kanamycin (J01GB04),
- LVX: levofloxacin (J01MA12),
- LIN: lincomycin (J01FF02),
- LNZ: linezolid (J01XX08),
- MEM: meropenem (J01DH02),
- MTR: metronidazole (J01XD01),
- MEZ: mezlocillin (J01CA10),
- MNO: minocycline (J01AA08),
- MFX: moxifloxacin (J01MA14),
- NAL: nalidixic acid (J01MB02),
- NEO: neomycin (J01GB05),
- NET: netilmicin (J01GB07),
- NIT: nitrofurantoin (J01XE01),
- NOR: norfloxacin (J01MA06),
- NOV: novobiocin (QJ01XX95),
- OFX: ofloxacin (J01MA01),
- OXA: oxacillin (J01CF04),
- PEN: penicillin G (J01CE01),
- PIP: piperacillin (J01CA12),
- TZP: piperacillin/tazobactam (J01CR05),
- PLB: polymyxin B (J01XB02),
- PRI: pristinamycin (J01FG01),
- PZA: pyrazinamide (J04AK01),
- QDA: quinupristin/dalfopristin (J01FG02),
- RIB: rifabutin (J04AB04),
- RIF: rifampicin (J04AB02),
- RFP: rifapentine (J04AB05),
- RXT: roxithromycin (J01FA06),
- SIS: sisomicin (J01GB08),
- STH: streptomycin-high (no ATC code),
- TEC: teicoplanin (J01XA02),
- TLV: telavancin (J01XA03),
- TCY: tetracycline (J01AA07),
- TIC: ticarcillin (J01CA13),
- TCC: ticarcillin/clavulanic acid (J01CR03),
- TGC: tigecycline (J01AA12),
- TOB: tobramycin (J01GB01),
- TMP: trimethoprim (J01EA01),
- SXT: trimethoprim/sulfamethoxazole (J01EE01),
- VAN: vancomycin (J01XA01).
            
+AMX: amoxicillin (J01CA04),
+AMC: amoxicillin/clavulanic acid (J01CR02),
+AMP: ampicillin (J01CA01),
+SAM: ampicillin/sulbactam (J01CR01),
+AZM: azithromycin (J01FA10),
+AZL: azlocillin (J01CA09),
+ATM: aztreonam (J01DF01),
+CAP: capreomycin (J04AB30),
+RID: cefaloridine (J01DB02),
+CZO: cefazolin (J01DB04),
+FEP: cefepime (J01DE01),
+CTX: cefotaxime (J01DD01),
+CTT: cefotetan (J01DC05),
+FOX: cefoxitin (J01DC01),
+CPT: ceftaroline (J01DI02),
+CAZ: ceftazidime (J01DD02),
+CRO: ceftriaxone (J01DD04),
+CXM: cefuroxime (J01DC02),
+CED: cephradine (J01DB09),
+CHL: chloramphenicol (J01BA01),
+CIP: ciprofloxacin (J01MA02),
+CLR: clarithromycin (J01FA09),
+CLI: clindamycin (J01FF01),
+COL: colistin (J01XB01),
+DAP: daptomycin (J01XX09),
+DOR: doripenem (J01DH04),
+DOX: doxycycline (J01AA02),
+ETP: ertapenem (J01DH03),
+ERY: erythromycin (J01FA01),
+ETH: ethambutol (J04AK02),
+FLC: flucloxacillin (J01CF05),
+FOS: fosfomycin (J01XX01),
+FUS: fusidic acid (J01XC01),
+GAT: gatifloxacin (J01MA16),
+GEN: gentamicin (J01GB03),
+GEH: gentamicin-high (no ATC code),
+IPM: imipenem (J01DH51),
+INH: isoniazid (J04AC01),
+KAN: kanamycin (J01GB04),
+LVX: levofloxacin (J01MA12),
+LIN: lincomycin (J01FF02),
+LNZ: linezolid (J01XX08),
+MEM: meropenem (J01DH02),
+MTR: metronidazole (J01XD01),
+MEZ: mezlocillin (J01CA10),
+MNO: minocycline (J01AA08),
+MFX: moxifloxacin (J01MA14),
+NAL: nalidixic acid (J01MB02),
+NEO: neomycin (J01GB05),
+NET: netilmicin (J01GB07),
+NIT: nitrofurantoin (J01XE01),
+NOR: norfloxacin (J01MA06),
+NOV: novobiocin (QJ01XX95),
+OFX: ofloxacin (J01MA01),
+OXA: oxacillin (J01CF04),
+PEN: penicillin G (J01CE01),
+PIP: piperacillin (J01CA12),
+TZP: piperacillin/tazobactam (J01CR05),
+PLB: polymyxin B (J01XB02),
+PRI: pristinamycin (J01FG01),
+PZA: pyrazinamide (J04AK01),
+QDA: quinupristin/dalfopristin (J01FG02),
+RIB: rifabutin (J04AB04),
+RIF: rifampicin (J04AB02),
+RFP: rifapentine (J04AB05),
+RXT: roxithromycin (J01FA06),
+SIS: sisomicin (J01GB08),
+STH: streptomycin-high (no ATC code),
+TEC: teicoplanin (J01XA02),
+TLV: telavancin (J01XA03),
+TCY: tetracycline (J01AA07),
+TIC: ticarcillin (J01CA13),
+TCC: ticarcillin/clavulanic acid (J01CR03),
+TGC: tigecycline (J01AA12),
+TOB: tobramycin (J01GB01),
+TMP: trimethoprim (J01EA01),
+SXT: trimethoprim/sulfamethoxazole (J01EE01),
+VAN: vancomycin (J01XA01).
            
     
            Read more on our website!
            
 
     
diff --git a/docs/reference/example_isolates.html b/docs/reference/example_isolates.html
index 03917626e..5db020555 100644
--- a/docs/reference/example_isolates.html
+++ b/docs/reference/example_isolates.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9033
+        0.8.0.9036
       
     
 
@@ -242,19 +242,18 @@
 
     
            Format
            
 
-    
            A data.frame with 2,000 observations and 49 variables:
            
-  
            date
            date of receipt at the laboratory
            
-  
            hospital_id
            ID of the hospital, from A to D
            
-  
            ward_icu
            logical to determine if ward is an intensive care unit
            
-  
            ward_clinical
            logical to determine if ward is a regular clinical ward
            
-  
            ward_outpatient
            logical to determine if ward is an outpatient clinic
            
-  
            age
            age of the patient
            
-  
            gender
            gender of the patient
            
-  
            patient_id
            ID of the patient
            
-  
            mo
            ID of microorganism created with as.mo, see also microorganisms
            
-  
            PEN:RIF
            40 different antibiotics with class rsi (see as.rsi); these column names occur in antibiotics data set and can be translated with ab_name
            
-
-
            
+    
            A data.frame with 2,000 observations and 49 variables:
              
+
            • date
               date of receipt at the laboratory
              • 
+
            • hospital_id
               ID of the hospital, from A to D
              • 
+
            • ward_icu
               logical to determine if ward is an intensive care unit
              • 
+
            • ward_clinical
               logical to determine if ward is a regular clinical ward
              • 
+
            • ward_outpatient
               logical to determine if ward is an outpatient clinic
              • 
+
            • age
               age of the patient
              • 
+
            • gender
               gender of the patient
              • 
+
            • patient_id
               ID of the patient
              • 
+
            • mo
               ID of microorganism created with as.mo(), see also microorganisms
              • 
+
            • PEN:RIF
               40 different antibiotics with class rsi (see as.rsi()); these column names occur in antibiotics data set and can be translated with ab_name()
              • 
+
            
     
            Read more on our website!
            
 
     
diff --git a/docs/reference/filter_ab_class.html b/docs/reference/filter_ab_class.html
index 25359ca93..2fbb21bdf 100644
--- a/docs/reference/filter_ab_class.html
+++ b/docs/reference/filter_ab_class.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -272,7 +272,7 @@
     
     
       ab_class
-      
            an antimicrobial class, like "carbapenems", as can be found in AMR::antibiotics$group
            
+      
            an antimicrobial class, like "carbapenems", as can be found in AMR::antibiotics$group
            
     
     
       result
@@ -290,7 +290,7 @@
 
     
            Details
            
 
-    
            The group column in antibiotics data set will be searched for ab_class (case-insensitive). If no results are found, the atc_group1 and atc_group2 columns will be searched. Next, x will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set.
            
+    
            The group column in antibiotics data set will be searched for ab_class (case-insensitive). If no results are found, the atc_group1 and atc_group2 columns will be searched. Next, x will be checked for column names with a value in any abbreviations, codes or official names found in the antibiotics data set.
            
 
     
            Examples
            
     
            library(dplyr)
diff --git a/docs/reference/first_isolate.html b/docs/reference/first_isolate.html
index 565ce03f6..405b293ad 100644
--- a/docs/reference/first_isolate.html
+++ b/docs/reference/first_isolate.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -237,26 +237,50 @@
     
            Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
            
     
 
-    
            first_isolate(x, col_date = NULL, col_patient_id = NULL,
-  col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
-  col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
-  testcodes_exclude = NULL, icu_exclude = FALSE,
-  specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
-  points_threshold = 2, info = TRUE, include_unknown = FALSE, ...)
+    
            first_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  col_testcode = NULL,
+  col_specimen = NULL,
+  col_icu = NULL,
+  col_keyantibiotics = NULL,
+  episode_days = 365,
+  testcodes_exclude = NULL,
+  icu_exclude = FALSE,
+  specimen_group = NULL,
+  type = "keyantibiotics",
+  ignore_I = TRUE,
+  points_threshold = 2,
+  info = TRUE,
+  include_unknown = FALSE,
+  ...
+)
 
-filter_first_isolate(x, col_date = NULL, col_patient_id = NULL,
-  col_mo = NULL, ...)
+filter_first_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  ...
+)
 
-filter_first_weighted_isolate(x, col_date = NULL,
-  col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
-  ...)
            
+filter_first_weighted_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  col_keyantibiotics = NULL,
+  ...
+)
            
 
     
            Arguments
            
     
-      
+      
@@ -268,7 +292,7 @@
     
-      
+      
@@ -284,7 +308,7 @@
     
-      
+      
@@ -324,28 +348,29 @@
     
-      
+      
            
     
     
            
       x
            a data.frame containing isolates.
            a data.frame containing isolates.
            		
     
            
     
            
       col_date
            
     
            
       col_mo
            column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
            column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
            		
     
            
     
            
       col_testcode
            
     
            
       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.
            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.
            		
     
            
     
            
       episode_days
            
     
            
       ...
            parameters passed on to the first_isolate function
            parameters passed on to the first_isolate() function
            		
     
            
     
            
 
     
            Source
            
 
-    
            Methodology of this function is based on: M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
            
+    
            Methodology of this function is based on:
            
+
            M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
            
     
            Value
            
 
-    
            Logical vector
            
+    
            A logical vector
            
     
            Details
            
 
     
            WHY THIS IS SO IMPORTANT 
            
-To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [1]. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.
            
+To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [1](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all S. aureus isolates would be overestimated, because you included this MRSA more than once. It would be selection bias.
            
 
            All isolates with a microbial ID of NA will be excluded as first isolate.
            
-
            The functions filter_first_isolate and filter_first_weighted_isolate are helper functions to quickly filter on first isolates. The function filter_first_isolate is essentially equal to:
            - x %>%
+
            The functions filter_first_isolate() and filter_first_weighted_isolate() are helper functions to quickly filter on first isolates. The function filter_first_isolate() is essentially equal to:
             x %>%
    mutate(only_firsts = first_isolate(x, ...)) %>%
    filter(only_firsts == TRUE) %>%
    select(-only_firsts)
-
            The function filter_first_weighted_isolate is essentially equal to:
            - x %>%
+
            
+
+
            The function filter_first_weighted_isolate() is essentially equal to:
             x %>%
    mutate(keyab = key_antibiotics(.)) %>%
    mutate(only_weighted_firsts = first_isolate(x,
                                                col_keyantibiotics = "keyab", ...)) %>%
@@ -357,11 +382,13 @@ To conduct an analysis of antimicrobial resistance, you should only include the
 
     
 
-
            There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results: 
            
-
            1. Using type = "keyantibiotics" and parameter ignore_I 
            
-    Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics function. 
            
-
            2. Using type = "points" and parameter points_threshold 
            
-    A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.
            
+
            There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results:
              
+
            1. Using type = "keyantibiotics" and parameter ignore_I
              
+
              Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics() function.
              2. 
+
            2. Using type = "points" and parameter points_threshold
              
+
              A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.
              3. 
+
            
+
     
            Read more on our website!
            
 
     
@@ -369,7 +396,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
 
            On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
            
     
            See also
            
 
-    
            key_antibiotics
            
+    
            key_antibiotics()
            
 
     
            Examples
            
     
            # `example_isolates` is a dataset available in the AMR package.
diff --git a/docs/reference/g.test.html b/docs/reference/g.test.html
index b3f4246ef..059034597 100644
--- a/docs/reference/g.test.html
+++ b/docs/reference/g.test.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,11 +234,10 @@
     
 
     
            
-    
            g.test performs chi-squared contingency table tests and goodness-of-fit tests, just like chisq.test but is more reliable [1]. A G-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a G-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called a G-test of independence).
            
+    
            g.test() performs chi-squared contingency table tests and goodness-of-fit tests, just like chisq.test() but is more reliable 1. A G-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a G-test of goodness-of-fit), or to see whether the proportions of one variable are different for different values of the other variable (called a G-test of independence).
            
     
            
 
-    
            g.test(x, y = NULL, p = rep(1/length(x), length(x)),
-  rescale.p = FALSE)
            
+    
            g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)
            
 
     
            Arguments
            
     
@@ -268,8 +267,8 @@
 
     
            Source
            
 
-    
            This code is almost identical to chisq.test, except that:
              
-
            • The calculation of the statistic was changed to 2 * sum(x * log(x / E))
              • 
+    
              The code for this function is identical to that of chisq.test(), except that:
                
+
              • The calculation of the statistic was changed to \(2 * sum(x * log(x / E))\)
                • 
 
              • Yates' continuity correction was removed as it does not apply to a G-test
                • 
 
              • The possibility to simulate p values with simulate.p.value was removed
                • 
 
              
@@ -301,33 +300,33 @@
 
              If x is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of x must be non-negative integers.  Otherwise, x and y must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these.  Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
              
 
              The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
              
 
              In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the G-test) but rather that for Fisher's exact test.
              
-
              In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by p, each sample being of size n = sum(x). This simulation is done in R and may be slow.
              
-    
              G-test of goodness-of-fit (likelihood ratio test)
              
+
              In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by p, each sample being of size n = sum(x). This simulation is done in R and may be slow.
              G-test of goodness-of-fit (likelihood ratio test)
              
 
-    
 
 
              Use the G-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).
              
-
              If the expected number of observations in any category is too small, the G-test may give inaccurate results, and you should use an exact test instead (fisher.test).
              
-
              The G-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (chisq.test); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
              
-    
              G-test of independence
              
+
              If the expected number of observations in any category is too small, the G-test may give inaccurate results, and you should use an exact test instead (fisher.test()).
              
+
              The G-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (chisq.test()); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
              
+
+
              G-test of independence
              
 
-    
 
 
              Use the G-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.
              
 
              It is also possible to do a G-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.
              
-
              Fisher's exact test (fisher.test) is an exact test, where the G-test is still only an approximation. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.
              
-
              The G-test of independence is an alternative to the chi-square test of independence (chisq.test), and they will give approximately the same results.
              
-    
              How the test works
              
+
              Fisher's exact test (fisher.test()) is an exact test, where the G-test is still only an approximation. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.
              
+
              The G-test of independence is an alternative to the chi-square test of independence (chisq.test()), and they will give approximately the same results.
              
 
-    
+
              How the test works
              
 
-
              Unlike the exact test of goodness-of-fit (fisher.test), the G-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the G-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
              
+
+
              Unlike the exact test of goodness-of-fit (fisher.test()), the G-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the G-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
              
 
              The G-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a G-statistic is:
              
-
              G <- 2 * sum(x * log(x / E))
              
-
              where E are the expected values. Since this is chi-square distributed, the p value can be calculated with:
              
-
              p <- stats::pchisq(G, df, lower.tail = FALSE)
              
+
              \(G = 2 * sum(x * log(x / E))\)
              
+
              where E are the expected values. Since this is chi-square distributed, the p value can be calculated in R with:
              p <- stats::pchisq(G, df, lower.tail = FALSE)
+
              
+
 
              where df are the degrees of freedom.
              
 
              If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use G-tests for each category, of course.
              
+
     
              Read more on our website!
              
 
     
@@ -335,10 +334,10 @@
 
              On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
              
     
              References
              
 
-    
              [1] McDonald, J.H. 2014. Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.
              
+    
              1 McDonald, J.H. 2014. Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.
              
     
              See also
              
 
-    
              chisq.test
              
+    
              chisq.test()
              
 
     
              Examples
              
     
              # = EXAMPLE 1 =
@@ -380,9 +379,6 @@
       
            • Source
              • 
       
            • Value
              • 
       
            • Details
              • 
-      
            • G-test of goodness-of-fit (likelihood ratio test)
              • 
-      
            • G-test of independence
              • 
-      
            • How the test works
              • 
       
            • Read more on our website!
              • 
       
            • References
              • 
       
            • See also
              • 
diff --git a/docs/reference/ggplot_rsi.html b/docs/reference/ggplot_rsi.html
index fb78880db..60b7ff6c3 100644
--- a/docs/reference/ggplot_rsi.html
+++ b/docs/reference/ggplot_rsi.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,43 +234,73 @@
     
 
     
              
-    
              Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal ggplot2 functions.
              
+    
              Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal ggplot2 functions.
              
     
              
 
-    
              ggplot_rsi(data, position = NULL, x = "antibiotic",
-  fill = "interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
-  limits = NULL, translate_ab = "name", combine_SI = TRUE,
-  combine_IR = FALSE, language = get_locale(), nrow = NULL,
-  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR =
-  "#ff6961", R = "#ff6961"), datalabels = TRUE, datalabels.size = 2.5,
-  datalabels.colour = "gray15", title = NULL, subtitle = NULL,
-  caption = NULL, x.title = "Antimicrobial", y.title = "Proportion",
-  ...)
+    
              ggplot_rsi(
+  data,
+  position = NULL,
+  x = "antibiotic",
+  fill = "interpretation",
+  facet = NULL,
+  breaks = seq(0, 1, 0.1),
+  limits = NULL,
+  translate_ab = "name",
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  language = get_locale(),
+  nrow = NULL,
+  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
+    "#ff6961"),
+  datalabels = TRUE,
+  datalabels.size = 2.5,
+  datalabels.colour = "gray15",
+  title = NULL,
+  subtitle = NULL,
+  caption = NULL,
+  x.title = "Antimicrobial",
+  y.title = "Proportion",
+  ...
+)
 
-geom_rsi(position = NULL, x = c("antibiotic", "interpretation"),
-  fill = "interpretation", translate_ab = "name",
-  language = get_locale(), combine_SI = TRUE, combine_IR = FALSE,
-  ...)
+geom_rsi(
+  position = NULL,
+  x = c("antibiotic", "interpretation"),
+  fill = "interpretation",
+  translate_ab = "name",
+  language = get_locale(),
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  ...
+)
 
 facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
 scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
 
-scale_rsi_colours(colours = c(S = "#61a8ff", SI = "#61a8ff", I =
-  "#61f7ff", IR = "#ff6961", R = "#ff6961"))
+scale_rsi_colours(
+  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
+    "#ff6961")
+)
 
 theme_rsi()
 
-labels_rsi_count(position = NULL, x = "antibiotic",
-  translate_ab = "name", combine_SI = TRUE, combine_IR = FALSE,
-  datalabels.size = 3, datalabels.colour = "gray15")
              
+labels_rsi_count(
+  position = NULL,
+  x = "antibiotic",
+  translate_ab = "name",
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  datalabels.size = 3,
+  datalabels.colour = "gray15"
+)
              
 
     
              Arguments
              
     
            
-      
+      
@@ -298,7 +328,7 @@
     
-      
+      
@@ -310,7 +340,7 @@
     
-      
+      
@@ -318,11 +348,11 @@
     
-      
+      
-      
+      
@@ -354,21 +384,23 @@
     
-      
+      
            
     
     
            
       data
            a data.frame with column(s) of class "rsi" (see as.rsi)
            a data.frame with column(s) of class rsi (see as.rsi())
            		
     
            
     
            
       position
            
     
            
       translate_ab
            a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property
            a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property()
            		
     
            
     
            
       combine_SI
            
     
            
       language
            language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
            language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
            		
     
            
     
            
       nrow
            
     
            
       colours
            a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be FALSE to use default ggplot2 colours.
            a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be FALSE to use default [ggplot2][ggplot2::ggplot() colours.
            		
     
            
     
            
       datalabels
            show datalabels using labels_rsi_count
            show datalabels using labels_rsi_count()
            		
     
            
     
            
       datalabels.size
            
     
            
       ...
            other parameters passed on to geom_rsi
            other parameters passed on to geom_rsi()
            		
     
            
     
            
 
     
            Details
            
 
-    
            At default, the names of antibiotics will be shown on the plots using ab_name. This can be set with the translate_ab parameter. See count_df.
            
-
            The functions
            
-geom_rsi will take any variable from the data that has an rsi class (created with as.rsi) using rsi_df and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
            
-
            facet_rsi creates 2d plots (at default based on S/I/R) using facet_wrap.
            
-
            scale_y_percent transforms the y axis to a 0 to 100% range using scale_continuous.
            
-
            scale_rsi_colours sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using scale_brewer.
            
-
            theme_rsi is a ggplot theme with minimal distraction.
            
-
            labels_rsi_count print datalabels on the bars with percentage and amount of isolates using geom_text
            
-
            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 (%>%). See Examples.
            
+    
            At default, the names of antibiotics will be shown on the plots using ab_name(). This can be set with the translate_ab parameter. See count_df().
            The functions
            
+
+
+
            geom_rsi() will take any variable from the data that has an rsi class (created with as.rsi()) using rsi_df() and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
            
+
            facet_rsi() creates 2d plots (at default based on S/I/R) using ggplot2::facet_wrap().
            
+
            scale_y_percent() transforms the y axis to a 0 to 100% range using ggplot2::scale_continuous().
            
+
            scale_rsi_colours() sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using ggplot2::scale_brewer().
            
+
            theme_rsi() is a [ggplot2 theme][ggplot2::theme() with minimal distraction.
            
+
            labels_rsi_count() print datalabels on the bars with percentage and amount of isolates using ggplot2::geom_text()
            
+
            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 (%>%). See Examples.
            
+
     
            Read more on our website!
            
 
     
diff --git a/docs/reference/guess_ab_col.html b/docs/reference/guess_ab_col.html
index ed78a93a0..4a279f99d 100644
--- a/docs/reference/guess_ab_col.html
+++ b/docs/reference/guess_ab_col.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
            
-    
            This tries to find a column name in a data set based on information from the antibiotics data set. Also supports WHONET abbreviations.
            
+    
            This tries to find a column name in a data set based on information from the antibiotics data set. Also supports WHONET abbreviations.
            
     
            
 
     
            guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE)
            
@@ -244,11 +244,11 @@
     
     
       x
-      
            a data.frame
            
+      
            a data.frame
            
     
     
       search_string
-      
            a text to search x for, will be checked with as.ab if this value is not a column in x
            
+      
            a text to search x for, will be checked with as.ab() if this value is not a column in x
            
     
     
       verbose
@@ -261,7 +261,7 @@
     
            A column name of x, or NULL when no result is found.
            
     
            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 precendence over shorter column names.
            
+    
            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 precendence over shorter column names.
            
     
            Read more on our website!
            
 
     
diff --git a/docs/reference/index.html b/docs/reference/index.html
index c5cb42912..5de7a7bbc 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -84,7 +84,7 @@
       
       
         AMR (for R)
-        0.8.0.9035
+        0.8.0.9036
       
     
 
@@ -348,7 +348,7 @@
         
           
            inner_join_microorganisms() left_join_microorganisms() right_join_microorganisms() full_join_microorganisms() semi_join_microorganisms() anti_join_microorganisms() 
            
         
-        
            Join a table with microorganisms
            
+        
            Join a table with microorganisms
            
       
         
         
@@ -372,7 +372,7 @@
         
           
            p_symbol() 
            
         
-        
            Symbol of a p value
            
+        
            Symbol of a p-value
            
       
     
       
@@ -398,7 +398,7 @@
         
           
            count_resistant() count_susceptible() count_R() count_IR() count_I() count_SI() count_S() count_all() n_rsi() count_df() 
            
         
-        
            Count isolates
            
+        
            Count available isolates
            
       
         
         
@@ -560,7 +560,7 @@
       
         
         
-          
            p.symbol() portion_R() portion_IR() portion_I() portion_SI() portion_S() 
            
+          
            p.symbol() portion_R() portion_IR() portion_I() portion_SI() portion_S() portion_df() 
            
         
         
            Deprecated functions
            
       
diff --git a/docs/reference/join.html b/docs/reference/join.html
index b27f061ef..ac2c22d1c 100644
--- a/docs/reference/join.html
+++ b/docs/reference/join.html
@@ -6,7 +6,7 @@
 
 
 
-Join a table with <code>microorganisms</code> — join • AMR (for R)
+Join a table with <a href='microorganisms.html'>microorganisms</a> — join • AMR (for R)
 
 
 
@@ -50,8 +50,8 @@
   
   
 
-
-
+
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -228,13 +228,13 @@
 
            
   
            
     
            
-    
            Join a table with microorganisms
            
+    
            Join a table with microorganisms
            
     
     
     
            
 
     
            
-    
            Join the dataset microorganisms easily to an existing table or character vector.
            
+    
            Join the data set microorganisms easily to an existing table or character vector.
            
     
            
 
     
            inner_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
@@ -258,7 +258,7 @@
     
     
       by
-      
            a variable to join by - if left empty will search for a column with class mo (created with as.mo) or will be "mo" if that column name exists in x, could otherwise be a column name of x with values that exist in microorganisms$mo (like by = "bacteria_id"), or another column in microorganisms (but then it should be named, like by = c("my_genus_species" = "fullname"))
            
+      
            a variable to join by - if left empty will search for a column with class mo (created with as.mo()) or will be "mo" if that column name exists in x, could otherwise be a column name of x with values that exist in microorganisms$mo (like by = "bacteria_id"), or another column in microorganisms (but then it should be named, like by = c("my_genus_species" = "fullname"))
            
     
     
       suffix
@@ -266,13 +266,13 @@
     
     
       ...
-      
            other parameters to pass on to dplyr::join.
            
+      
            other parameters to pass on to dplyr::join()
            
     
     
 
     
            Details
            
 
-    
            Note: As opposed to the join functions of dplyr, characters vectors are supported and at default existing columns will get a suffix "2" and the newly joined columns will not get a suffix. See join for more information.
            
+    
            Note: As opposed to the dplyr::join() functions of dplyr, characters vectors are supported and at default existing columns will get a suffix "2" and the newly joined columns will not get a suffix. See dplyr::join() for more information.
            
     
            Read more on our website!
            
 
     
diff --git a/docs/reference/key_antibiotics.html b/docs/reference/key_antibiotics.html
index f79575eab..ca78ccffe 100644
--- a/docs/reference/key_antibiotics.html
+++ b/docs/reference/key_antibiotics.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
            
 
@@ -234,28 +234,42 @@
     
            
 
     
            
-    
            These function can be used to determine first isolates (see first_isolate). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first weighted isolates.
            
+    
            These function can be used to determine first isolates (see first_isolate()). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first weighted isolates.
            
     
            
 
-    
            key_antibiotics(x, col_mo = NULL, universal_1 = guess_ab_col(x,
-  "amoxicillin"), universal_2 = guess_ab_col(x,
-  "amoxicillin/clavulanic acid"), universal_3 = guess_ab_col(x,
-  "cefuroxime"), universal_4 = guess_ab_col(x,
-  "piperacillin/tazobactam"), universal_5 = guess_ab_col(x,
-  "ciprofloxacin"), universal_6 = guess_ab_col(x,
-  "trimethoprim/sulfamethoxazole"), GramPos_1 = guess_ab_col(x,
-  "vancomycin"), GramPos_2 = guess_ab_col(x, "teicoplanin"),
+    
            key_antibiotics(
+  x,
+  col_mo = NULL,
+  universal_1 = guess_ab_col(x, "amoxicillin"),
+  universal_2 = guess_ab_col(x, "amoxicillin/clavulanic acid"),
+  universal_3 = guess_ab_col(x, "cefuroxime"),
+  universal_4 = guess_ab_col(x, "piperacillin/tazobactam"),
+  universal_5 = guess_ab_col(x, "ciprofloxacin"),
+  universal_6 = guess_ab_col(x, "trimethoprim/sulfamethoxazole"),
+  GramPos_1 = guess_ab_col(x, "vancomycin"),
+  GramPos_2 = guess_ab_col(x, "teicoplanin"),
   GramPos_3 = guess_ab_col(x, "tetracycline"),
   GramPos_4 = guess_ab_col(x, "erythromycin"),
-  GramPos_5 = guess_ab_col(x, "oxacillin"), GramPos_6 = guess_ab_col(x,
-  "rifampin"), GramNeg_1 = guess_ab_col(x, "gentamicin"),
+  GramPos_5 = guess_ab_col(x, "oxacillin"),
+  GramPos_6 = guess_ab_col(x, "rifampin"),
+  GramNeg_1 = guess_ab_col(x, "gentamicin"),
   GramNeg_2 = guess_ab_col(x, "tobramycin"),
-  GramNeg_3 = guess_ab_col(x, "colistin"), GramNeg_4 = guess_ab_col(x,
-  "cefotaxime"), GramNeg_5 = guess_ab_col(x, "ceftazidime"),
-  GramNeg_6 = guess_ab_col(x, "meropenem"), warnings = TRUE, ...)
+  GramNeg_3 = guess_ab_col(x, "colistin"),
+  GramNeg_4 = guess_ab_col(x, "cefotaxime"),
+  GramNeg_5 = guess_ab_col(x, "ceftazidime"),
+  GramNeg_6 = guess_ab_col(x, "meropenem"),
+  warnings = TRUE,
+  ...
+)
 
-key_antibiotics_equal(y, z, type = c("keyantibiotics", "points"),
-  ignore_I = TRUE, points_threshold = 2, info = FALSE)
            
+key_antibiotics_equal(
+  y,
+  z,
+  type = c("keyantibiotics", "points"),
+  ignore_I = TRUE,
+  points_threshold = 2,
+  info = FALSE
+)
            
 
     
            Arguments
            
     
@@ -266,19 +280,19 @@
     
-      
+      
-      
+      
-      
+      
-      
+      
@@ -312,22 +326,49 @@
 
     
            Details
            
 
-    
            The function key_antibiotics returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it would not.
            
-
            At default, the antibiotics that are used for Gram-positive bacteria are: 
            
-  amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampin.
            
-
            At default, the antibiotics that are used for Gram-negative bacteria are: 
            
-  amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem.
            
+    
            The function key_antibiotics() returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal(), to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."). The first_isolate() function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate()). Without key antibiotic comparison it would not.
            
+
            At default, the antibiotics that are used for Gram-positive bacteria are:
              
+
            • Amoxicillin
              • 
+
            • Amoxicillin/clavulanic acid
              • 
+
            • Cefuroxime
              • 
+
            • Piperacillin/tazobactam
              • 
+
            • Ciprofloxacin
              • 
+
            • Trimethoprim/sulfamethoxazole
              • 
+
            • Vancomycin
              • 
+
            • Teicoplanin
              • 
+
            • Tetracycline
              • 
+
            • Erythromycin
              • 
+
            • Oxacillin
              • 
+
            • Rifampin
              • 
+
            
 
-
            The function key_antibiotics_equal checks the characters returned by key_antibiotics for equality, and returns a logical vector.
            
+
            At default the antibiotics that are used for Gram-negative bacteria are:
              
+
            • Amoxicillin
              • 
+
            • Amoxicillin/clavulanic acid
              • 
+
            • Cefuroxime
              • 
+
            • Piperacillin/tazobactam
              • 
+
            • Ciprofloxacin
              • 
+
            • Trimethoprim/sulfamethoxazole
              • 
+
            • Gentamicin
              • 
+
            • Tobramycin
              • 
+
            • Colistin
              • 
+
            • Cefotaxime
              • 
+
            • Ceftazidime
              • 
+
            • Meropenem
              • 
+
            
+
+
            The function key_antibiotics_equal() checks the characters returned by key_antibiotics() for equality, and returns a logical vector.
            Key antibiotics
            
 
     
 
-
            There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results: 
            
-
            1. Using type = "keyantibiotics" and parameter ignore_I 
            
-    Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics function. 
            
-
            2. Using type = "points" and parameter points_threshold 
            
-    A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.
            
+
            There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results:
              
+
            1. Using type = "keyantibiotics" and parameter ignore_I
              
+
              Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics() function.
              2. 
+
            2. Using type = "points" and parameter points_threshold
              
+
              A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.
              3. 
+
            
+
     
            Read more on our website!
            
 
     
@@ -335,7 +376,7 @@
 
            On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
            See also
            
 
-    
            first_isolate
            
+    
            first_isolate()
            Examples
            # `example_isolates` is a dataset available in the AMR package.
diff --git a/docs/reference/kurtosis.html b/docs/reference/kurtosis.html
index 29b192872..68b766cc0 100644
--- a/docs/reference/kurtosis.html
+++ b/docs/reference/kurtosis.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -253,7 +253,7 @@
     
            
-      
+      
@@ -268,7 +268,7 @@
 
            On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
            See also
            
 
-    
            skewness
            
+    
            skewness()
            
 
@@ -234,7 +234,7 @@
     
            
-    
            Convenient wrapper around grep to match a pattern: a %like% b. It always returns a logical vector and is always case-insensitive (use a %like_case% b for case-sensitive matching). Also, pattern (b) can be as long as x (a) to compare items of each index in both vectors, or can both have the same length to iterate over all cases.
            
+    
            Convenient wrapper around base::grep() to match a pattern: a %like% b. It always returns a logical vector and is always case-insensitive (use a %like_case% b for case-sensitive matching). Also, pattern (b) can be as long as x (a) to compare items of each index in both vectors, or can both have the same length to iterate over all cases.
            
     
            like(x, pattern, ignore.case = TRUE)
@@ -274,10 +274,10 @@
     
            Idea from the like function from the data.table package, but made it case insensitive at default and let it support multiple patterns. Also, if the regex fails the first time, it tries again with perl = TRUE.
            
     
            Value
            
 
-    
            A logical vector
            
+    
            A logical vector
            
     
            Details
            
 
-    
            Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
            
+    
            Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
            
     
            Read more on our website!
            
 
     
@@ -285,7 +285,7 @@
 
            On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
            
     
            See also
            
 
-    
            grep
            
+    
            base::grep()
            
 
     
            Examples
            
     
            # simple test
diff --git a/docs/reference/mdro.html b/docs/reference/mdro.html
index f1d5c40dc..a36960441 100644
--- a/docs/reference/mdro.html
+++ b/docs/reference/mdro.html
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9033
+        0.8.0.9036
       
     
 
@@ -237,9 +237,16 @@
     
            Determine which isolates are multidrug-resistant organisms (MDRO) according to international and national guidelines.
            
     
 
-    
            mdro(x, guideline = NULL, col_mo = NULL, info = TRUE,
-  pct_required_classes = 0.5, combine_SI = TRUE, verbose = FALSE,
-  ...)
+    
            mdro(
+  x,
+  guideline = "CMI2012",
+  col_mo = NULL,
+  info = TRUE,
+  pct_required_classes = 0.5,
+  combine_SI = TRUE,
+  verbose = FALSE,
+  ...
+)
 
 brmo(x, guideline = "BRMO", ...)
 
@@ -260,15 +267,15 @@
     
            
-      
+      
-      
+      
-      
+      
@@ -284,32 +291,41 @@
     
-      
+      
            
     
            
       col_mo
            column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
            column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
            		
     
            
     
            
       universal_1, universal_2, universal_3, universal_4, universal_5, universal_6
            column names of broad-spectrum antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
            column names of broad-spectrum antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col().
            		
     
            
     
            
       GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6
            column names of antibiotics for Gram-positives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
            column names of antibiotics for Gram-positives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col().
            		
     
            
     
            
       GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6
            column names of antibiotics for Gram-negatives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col.
            column names of antibiotics for Gram-negatives, case-insensitive. At default, the columns containing these antibiotics will be guessed with guess_ab_col().
            		
     
            
     
            
       warnings
     
     
 
     
     
            
     
            
       x
            a vector of values, a matrix or a data frame
            a vector of values, a matrix or a data frame
            		
     
            
     
            
       na.rm
     
 
   
   
 
     
 
     
            
     
            
       guideline
            a specific guideline to follow. When left empty, the publication by Magiorakos et al. (2012, Clinical Microbiology and Infection) will be followed, see Details.
            a specific guideline to follow. When left empty, the publication by Magiorakos et al. (2012, Clinical Microbiology and Infection) will be followed, please see Details.
            		
     
            
     
            
       col_mo
            column name of the IDs of the microorganisms (see as.mo), defaults to the first column of class mo. Values will be coerced using as.mo.
            column name of the IDs of the microorganisms (see as.mo()), defaults to the first column of class mo. Values will be coerced using as.mo().
            		
     
            
     
            
       info
            print progress
            a logical to indicate whether progress should be printed to the console
            		
     
            
     
            
       pct_required_classes
            
     
            
       ...
            column name of an antibiotic, see section Antibiotics
            column name of an antibiotic, please see section Antibiotics below
            		
     
            
     
            
 
     
            Source
            
 
-    
            Please see Details for the list of publications used for this function.
            
+    
            Please see Details for the list of publications used for this function.
            
     
            Value
            
 
     
 
              
-
            • CMI 2012 paper - function mdr_cmi2012() or mdro():
               Ordered factor with levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)
              • 
-
            • TB guideline - function mdr_tb() or mdro(..., guideline = "TB"):
               Ordered factor with levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant
              • 
-
            • German guideline - function mrgn() or mdro(..., guideline = "MRGN"):
               Ordered factor with levels Negative < 3MRGN < 4MRGN
              • 
-
            • Everything else:
               Ordered factor with levels Negative < Positive, unconfirmed < Positive. The value "Positive, unconfirmed" means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests
              • 
+
            • CMI 2012 paper - function mdr_cmi2012() or mdro():
              
+Ordered factor with levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)
              • 
+
            • TB guideline - function mdr_tb() or mdro(..., guideline = "TB"):
              
+Ordered factor with levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant
              • 
+
            • German guideline - function mrgn() or mdro(..., guideline = "MRGN"):
              
+Ordered factor with levels Negative < 3MRGN < 4MRGN
              • 
+
            • Everything else:
              
+Ordered factor with levels Negative < Positive, unconfirmed < Positive. The value "Positive, unconfirmed" means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests
              • 
 
            
 
     
            Details
            
 
     
            For the pct_required_classes argument, values above 1 will be divided by 100. This is to support both fractions (0.75 or 3/4) and percentages (75).
            
 
            Currently supported guidelines are (case-insensitive):
              
-
            • guideline = "CMI2012": Magiorakos AP, Srinivasan A et al. "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (link)
              • 
-
            • guideline = "EUCAST": The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (link)
              • 
-
            • guideline = "TB": The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (link)
              • 
-
            • guideline = "MRGN": The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
              • 
-
            • guideline = "BRMO": The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" (link)
              • 
+
            • guideline = "CMI2012"
              
+Magiorakos AP, Srinivasan A et al. "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (link)
              • 
+
            • guideline = "EUCAST"
              
+The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (link)
              • 
+
            • guideline = "TB"
              
+The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (link)
              • 
+
            • guideline = "MRGN"
              
+The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
              • 
+
            • guideline = "BRMO"
              
+The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) ZKH" (link)
              • 
 
            
 
 
            Please suggest your own (country-specific) guidelines by letting us know: https://gitlab.com/msberends/AMR/issues/new.
            
@@ -318,100 +334,99 @@
 
     
 
-
            To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
            
-
            The following antibiotics are used for the functions eucast_rules and mdro. These are shown below in the format 'antimicrobial ID: name (ATC code)', sorted by name:
            
+
            To define antibiotics column names, leave as it is to determine it automatically with guess_ab_col() or input a text (case-insensitive), or use NULL to skip a column (e.g. TIC = NULL to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
            
+
            The following antibiotics are used for the functions eucast_rules() and mdro(). These are shown below in the format 'antimicrobial ID: name (ATC code)', sorted by name:
            
 
            AMK: amikacin (J01GB06),
- AMX: amoxicillin (J01CA04),
- AMC: amoxicillin/clavulanic acid (J01CR02),
- AMP: ampicillin (J01CA01),
- SAM: ampicillin/sulbactam (J01CR01),
- AZM: azithromycin (J01FA10),
- AZL: azlocillin (J01CA09),
- ATM: aztreonam (J01DF01),
- CAP: capreomycin (J04AB30),
- RID: cefaloridine (J01DB02),
- CZO: cefazolin (J01DB04),
- FEP: cefepime (J01DE01),
- CTX: cefotaxime (J01DD01),
- CTT: cefotetan (J01DC05),
- FOX: cefoxitin (J01DC01),
- CPT: ceftaroline (J01DI02),
- CAZ: ceftazidime (J01DD02),
- CRO: ceftriaxone (J01DD04),
- CXM: cefuroxime (J01DC02),
- CED: cephradine (J01DB09),
- CHL: chloramphenicol (J01BA01),
- CIP: ciprofloxacin (J01MA02),
- CLR: clarithromycin (J01FA09),
- CLI: clindamycin (J01FF01),
- COL: colistin (J01XB01),
- DAP: daptomycin (J01XX09),
- DOR: doripenem (J01DH04),
- DOX: doxycycline (J01AA02),
- ETP: ertapenem (J01DH03),
- ERY: erythromycin (J01FA01),
- ETH: ethambutol (J04AK02),
- FLC: flucloxacillin (J01CF05),
- FOS: fosfomycin (J01XX01),
- FUS: fusidic acid (J01XC01),
- GAT: gatifloxacin (J01MA16),
- GEN: gentamicin (J01GB03),
- GEH: gentamicin-high (no ATC code),
- IPM: imipenem (J01DH51),
- INH: isoniazid (J04AC01),
- KAN: kanamycin (J01GB04),
- LVX: levofloxacin (J01MA12),
- LIN: lincomycin (J01FF02),
- LNZ: linezolid (J01XX08),
- MEM: meropenem (J01DH02),
- MTR: metronidazole (J01XD01),
- MEZ: mezlocillin (J01CA10),
- MNO: minocycline (J01AA08),
- MFX: moxifloxacin (J01MA14),
- NAL: nalidixic acid (J01MB02),
- NEO: neomycin (J01GB05),
- NET: netilmicin (J01GB07),
- NIT: nitrofurantoin (J01XE01),
- NOR: norfloxacin (J01MA06),
- NOV: novobiocin (QJ01XX95),
- OFX: ofloxacin (J01MA01),
- OXA: oxacillin (J01CF04),
- PEN: penicillin G (J01CE01),
- PIP: piperacillin (J01CA12),
- TZP: piperacillin/tazobactam (J01CR05),
- PLB: polymyxin B (J01XB02),
- PRI: pristinamycin (J01FG01),
- PZA: pyrazinamide (J04AK01),
- QDA: quinupristin/dalfopristin (J01FG02),
- RIB: rifabutin (J04AB04),
- RIF: rifampicin (J04AB02),
- RFP: rifapentine (J04AB05),
- RXT: roxithromycin (J01FA06),
- SIS: sisomicin (J01GB08),
- STH: streptomycin-high (no ATC code),
- TEC: teicoplanin (J01XA02),
- TLV: telavancin (J01XA03),
- TCY: tetracycline (J01AA07),
- TIC: ticarcillin (J01CA13),
- TCC: ticarcillin/clavulanic acid (J01CR03),
- TGC: tigecycline (J01AA12),
- TOB: tobramycin (J01GB01),
- TMP: trimethoprim (J01EA01),
- SXT: trimethoprim/sulfamethoxazole (J01EE01),
- VAN: vancomycin (J01XA01).
            
+AMX: amoxicillin (J01CA04),
+AMC: amoxicillin/clavulanic acid (J01CR02),
+AMP: ampicillin (J01CA01),
+SAM: ampicillin/sulbactam (J01CR01),
+AZM: azithromycin (J01FA10),
+AZL: azlocillin (J01CA09),
+ATM: aztreonam (J01DF01),
+CAP: capreomycin (J04AB30),
+RID: cefaloridine (J01DB02),
+CZO: cefazolin (J01DB04),
+FEP: cefepime (J01DE01),
+CTX: cefotaxime (J01DD01),
+CTT: cefotetan (J01DC05),
+FOX: cefoxitin (J01DC01),
+CPT: ceftaroline (J01DI02),
+CAZ: ceftazidime (J01DD02),
+CRO: ceftriaxone (J01DD04),
+CXM: cefuroxime (J01DC02),
+CED: cephradine (J01DB09),
+CHL: chloramphenicol (J01BA01),
+CIP: ciprofloxacin (J01MA02),
+CLR: clarithromycin (J01FA09),
+CLI: clindamycin (J01FF01),
+COL: colistin (J01XB01),
+DAP: daptomycin (J01XX09),
+DOR: doripenem (J01DH04),
+DOX: doxycycline (J01AA02),
+ETP: ertapenem (J01DH03),
+ERY: erythromycin (J01FA01),
+ETH: ethambutol (J04AK02),
+FLC: flucloxacillin (J01CF05),
+FOS: fosfomycin (J01XX01),
+FUS: fusidic acid (J01XC01),
+GAT: gatifloxacin (J01MA16),
+GEN: gentamicin (J01GB03),
+GEH: gentamicin-high (no ATC code),
+IPM: imipenem (J01DH51),
+INH: isoniazid (J04AC01),
+KAN: kanamycin (J01GB04),
+LVX: levofloxacin (J01MA12),
+LIN: lincomycin (J01FF02),
+LNZ: linezolid (J01XX08),
+MEM: meropenem (J01DH02),
+MTR: metronidazole (J01XD01),
+MEZ: mezlocillin (J01CA10),
+MNO: minocycline (J01AA08),
+MFX: moxifloxacin (J01MA14),
+NAL: nalidixic acid (J01MB02),
+NEO: neomycin (J01GB05),
+NET: netilmicin (J01GB07),
+NIT: nitrofurantoin (J01XE01),
+NOR: norfloxacin (J01MA06),
+NOV: novobiocin (QJ01XX95),
+OFX: ofloxacin (J01MA01),
+OXA: oxacillin (J01CF04),
+PEN: penicillin G (J01CE01),
+PIP: piperacillin (J01CA12),
+TZP: piperacillin/tazobactam (J01CR05),
+PLB: polymyxin B (J01XB02),
+PRI: pristinamycin (J01FG01),
+PZA: pyrazinamide (J04AK01),
+QDA: quinupristin/dalfopristin (J01FG02),
+RIB: rifabutin (J04AB04),
+RIF: rifampicin (J04AB02),
+RFP: rifapentine (J04AB05),
+RXT: roxithromycin (J01FA06),
+SIS: sisomicin (J01GB08),
+STH: streptomycin-high (no ATC code),
+TEC: teicoplanin (J01XA02),
+TLV: telavancin (J01XA03),
+TCY: tetracycline (J01AA07),
+TIC: ticarcillin (J01CA13),
+TCC: ticarcillin/clavulanic acid (J01CR03),
+TGC: tigecycline (J01AA12),
+TOB: tobramycin (J01GB01),
+TMP: trimethoprim (J01EA01),
+SXT: trimethoprim/sulfamethoxazole (J01EE01),
+VAN: vancomycin (J01XA01).
            
     
            Interpretation of S, I and R
            
 
     
 
-
            In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
            
-
              
+
              In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                
 
              • S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
                • 
 
              • I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
                • 
 
              • R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
                • 
 
              
 
 
              Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
              
-
              This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
              
+
              This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
              
     
              Read more on our website!
              
 
     
diff --git a/docs/reference/microorganisms.codes.html b/docs/reference/microorganisms.codes.html
index 73cc3ae24..43f207313 100644
--- a/docs/reference/microorganisms.codes.html
+++ b/docs/reference/microorganisms.codes.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9035
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
              
-    
              A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with set_mo_source.
              
+    
              A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with set_mo_source().
              
     
              
 
     
              microorganisms.codes
              
@@ -242,18 +242,17 @@
 
     
              Format
              
 
-    
              A data.frame with 5,433 observations and 2 variables:
              
-  
              code
              Commonly used code of a microorganism
              
-  
              mo
              ID of the microorganism in the microorganisms data set
              
-
-
              
+    
              A data.frame with 5,433 observations and 2 variables:
                
+
              • code
                 Commonly used code of a microorganism
                • 
+
              • mo
                 ID of the microorganism in the microorganisms data set
                • 
+
              
     
              Catalogue of Life
              
 
     
 
 
               
              
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
              
-
              Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
              
+
              Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
              
     
              Read more on our website!
              
 
     
@@ -261,7 +260,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
              On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
              
     
              See also
              
 
-    
              as.mo microorganisms
              
+    
              as.mo() microorganisms
              
 
   
   
 
@@ -234,7 +234,7 @@
     
 
     
              
-    
              A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using as.mo.
              
+    
              A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using as.mo().
              
     
              
 
     
              microorganisms
              
@@ -242,27 +242,26 @@
 
     
              Format
              
 
-    
              A data.frame with 69,447 observations and 16 variables:
              
-  
              mo
              ID of microorganism as used by this package
              
-  
              col_id
              Catalogue of Life ID
              
-  
              fullname
              Full name, like "Escherichia coli"
              
-  
              kingdom, phylum, class, order, family, genus, species, subspecies
              Taxonomic rank of the microorganism
              
-  
              rank
              Text of the taxonomic rank of the microorganism, like "species" or "genus"
              
-  
              ref
              Author(s) and year of concerning scientific publication
              
-  
              species_id
              ID of the species as used by the Catalogue of Life
              
-  
              source
              Either "CoL", "DSMZ" (see Source) or "manually added"
              
-  
              prevalence
              Prevalence of the microorganism, see ?as.mo
              
-
-
              
+    
              A data.frame with 69,447 observations and 16 variables:
                
+
              • mo
                 ID of microorganism as used by this package
                • 
+
              • col_id
                 Catalogue of Life ID
                • 
+
              • fullname
                 Full name, like "Escherichia coli"
                • 
+
              • kingdom, phylum, class, order, family, genus, species, subspecies
                 Taxonomic rank of the microorganism
                • 
+
              • rank
                 Text of the taxonomic rank of the microorganism, like "species" or "genus"
                • 
+
              • ref
                 Author(s) and year of concerning scientific publication
                • 
+
              • species_id
                 ID of the species as used by the Catalogue of Life
                • 
+
              • source
                 Either "CoL", "DSMZ" (see Source) or "manually added"
                • 
+
              • prevalence
                 Prevalence of the microorganism, see as.mo()
                • 
+
              
     
              Source
              
 
-    
              Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
              
-
              Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date (check included version with catalogue_of_life_version()).
              
+    
              Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
              
+
              Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date (check included version with catalogue_of_life_version()).
              
     
              Details
              
 
     
              Manually added were:
                
 
              • 11 entries of Streptococcus (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
                • 
-
              • 2 entries of Staphylococcus (coagulase-negative [CoNS] and coagulase-positive [CoPS])
                • 
+
              • 2 entries of Staphylococcus (coagulase-negative CoNS and coagulase-positive CoPS)
                • 
 
              • 3 entries of Trichomonas (Trichomonas vaginalis, and its family and genus)
                • 
 
              • 1 entry of Blastocystis (Blastocystis hominis), although it officially does not exist (Noel et al. 2005, PMID 15634993)
                • 
 
              • 5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)
                • 
@@ -282,7 +281,7 @@
 
 
                 
                
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
                
-
                Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                
+
                Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                
     
                Read more on our website!
                
 
     
@@ -290,7 +289,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
                On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                
     
                See also
                
 
-    
                as.mo, mo_property, microorganisms.codes
                
+    
                as.mo(), mo_property(), microorganisms.codes
                
 
   
   
 
@@ -234,7 +234,7 @@
     
 
     
                
-    
                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.
                
+    
                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().
                
     
                
 
     
                microorganisms.old
                
@@ -242,24 +242,23 @@
 
     
                Format
                
 
-    
                A data.frame with 24,246 observations and 5 variables:
                
-  
                col_id
                Catalogue of Life ID that was originally given
                
-  
                col_id_new
                New Catalogue of Life ID that responds to an entry in the microorganisms data set
                
-  
                fullname
                Old full taxonomic name of the microorganism
                
-  
                ref
                Author(s) and year of concerning scientific publication
                
-  
                prevalence
                Prevalence of the microorganism, see ?as.mo
                
-
-
                
+    
                A data.frame with 24,246 observations and 5 variables:
                  
+
                • col_id
                   Catalogue of Life ID that was originally given
                  • 
+
                • col_id_new
                   New Catalogue of Life ID that responds to an entry in the microorganisms data set
                  • 
+
                • fullname
                   Old full taxonomic name of the microorganism
                  • 
+
                • ref
                   Author(s) and year of concerning scientific publication
                  • 
+
                • prevalence
                   Prevalence of the microorganism, see as.mo()
                  • 
+
                
     
                Source
                
 
-    
                Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
                
+    
                Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
                
     
                Catalogue of Life
                
 
     
 
 
                 
                
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
                
-
                Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                
+
                Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                
     
                Read more on our website!
                
 
     
@@ -267,7 +266,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
                On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                
     
                See also
                
 
-    
                as.mo mo_property microorganisms
                
+    
                as.mo() mo_property() microorganisms
                
 
   
   
 
@@ -234,7 +234,7 @@
     
 
     
                
-    
                Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with as.mo, which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples.
                
+    
                Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with as.mo(), which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples.
                
     
                
 
     
                mo_name(x, language = get_locale(), ...)
@@ -286,23 +286,23 @@
     
     
       x
-      
                any (vector of) text that can be coerced to a valid microorganism code with as.mo
                
+      
                any (vector of) text that can be coerced to a valid microorganism code with as.mo()
                
     
     
       language
-      
                language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
                
+      
                language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
                
     
     
       ...
-      
                other parameters passed on to as.mo
                
+      
                other parameters passed on to as.mo()
                
     
     
       open
-      
                browse the URL using browseURL()
                
+      
                browse the URL using utils::browseURL()
                
     
     
       property
-      
                one of the column names of the microorganisms data set or "shortname"
                
+      
                one of the column names of the microorganisms data set or "shortname"
                
     
     
 
@@ -310,15 +310,15 @@
 
     
 
                  
-
                • An integer in case of mo_year
                  • 
-
                • A list in case of mo_taxonomy
                  • 
-
                • A named character in case of mo_url
                  • 
-
                • A character in all other cases
                  • 
+
                • An integer in case of mo_year()
                  • 
+
                • A list in case of mo_taxonomy()
                  • 
+
                • A named character in case of mo_url()
                  • 
+
                • A character in all other cases
                  • 
 
                
 
     
                Details
                
 
-    
                All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for mo_ref, mo_authors and mo_year. This leads to the following results:
                  
+    
                  All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for mo_ref(), mo_authors() and mo_year(). This leads to the following results:
                    
 
                  • mo_name("Chlamydia psittaci") will return "Chlamydophila psittaci" (with a warning about the renaming)
                    • 
 
                  • mo_ref("Chlamydia psittaci") will return "Page, 1968" (with a warning about the renaming)
                    • 
 
                  • mo_ref("Chlamydophila psittaci") will return "Everett et al., 1999" (without a warning)
                    • 
@@ -333,15 +333,18 @@
 
 
                     
                    
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (http://www.catalogueoflife.org). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
                    
-
                    Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                    
+
                    Click here for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with catalogue_of_life_version().
                    
     
                    Source
                    
 
     
 
-
                    [1] Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13
                    
-
                    [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. https://doi.org/10.1016/j.cmi.2019.02.028
                    
-
                    [3] Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
                    
-
                    [4] Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
                    
+
                      
+
                    1. Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870–926. https://dx.doi.org/10.1128/CMR.00109-13
                      2. 
+
                    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. https://doi.org/10.1016/j.cmi.2019.02.028
                      3. 
+
                    3. Lancefield RC A serological differentiation of human and other groups of hemolytic streptococci. 1933. J Exp Med. 57(4): 571–95. https://dx.doi.org/10.1084/jem.57.4.571
                      4. 
+
                    4. Catalogue of Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version with catalogue_of_life_version()).
                      5. 
+
                    
+
     
                    Read more on our website!
                    
 
     
@@ -349,7 +352,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
                    On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                    
     
                    See also
                    
 
-    
                    microorganisms
                    
+    
                    microorganisms
                    
 
     
                    Examples
                    
     
                    # taxonomic tree -----------------------------------------------------------
@@ -431,7 +434,7 @@ This package contains the complete taxonomic tree of almost all microorganisms (
 
 # get a list with the complete taxonomy (from kingdom to subspecies)
 mo_taxonomy("E. coli")
-# get a list with the taxonomy, the authors and the URL to the online database
+# get a list with the taxonomy, the authors, Gram-stain and URL to the online database
 mo_info("E. coli")
 # }
                    
   
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html
index 161ded70b..7e9f0944c 100644
--- a/docs/reference/mo_source.html
+++ b/docs/reference/mo_source.html
@@ -51,7 +51,7 @@
   
 
 
-
 
 
@@ -86,7 +86,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -235,7 +235,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     
 
     
                    
-    
                    These functions can be used to predefine your own reference to be used in as.mo and consequently all mo_* functions like mo_genus and mo_gramstain.
                    
+    
                    These functions can be used to predefine your own reference to be used in as.mo() and consequently all mo_* functions like mo_genus() and mo_gramstain().
                    
 
                    This is the fastest way to have your organisation (or analysis) specific codes picked up and translated by this package.
                    
     
                    
 
@@ -255,15 +255,12 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     
                    Details
                    
 
     
                    The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the readxl package installed.
                    
-
                    set_mo_source will check the file for validity: it must be a data.frame, must have a column named "mo" which contains values from microorganisms$mo and must have a reference column with your own defined values. If all tests pass, set_mo_source will read the file into R and export it to "~/.mo_source.rds". This compressed data file will then be used at default for MO determination (function as.mo and consequently all mo_* functions like mo_genus and mo_gramstain). The location of the original file will be saved as option with options(mo_source = path). Its timestamp will be saved with options(mo_source_datetime = ...).
                    
-
                    get_mo_source will return the data set by reading "~/.mo_source.rds" with readRDS. If the original file has changed (the file defined with path), it will call set_mo_source to update the data file automatically.
                    
-
                    Reading an Excel file (.xlsx) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by get_mo_source in only a couple of microseconds (a millionth of a second).
                    
-    
                    How it works
                    
+
                    set_mo_source() will check the file for validity: it must be a data.frame, must have a column named "mo" which contains values from microorganisms$mo and must have a reference column with your own defined values. If all tests pass, set_mo_source() will read the file into R and export it to "~/.mo_source.rds". This compressed data file will then be used at default for MO determination (function as.mo() and consequently all mo_* functions like mo_genus() and mo_gramstain()). The location of the original file will be saved as option with options(mo_source = path). Its timestamp will be saved with options(mo_source_datetime = ...).
                    
+
                    get_mo_source() will return the data set by reading "~/.mo_source.rds" with readRDS(). If the original file has changed (the file defined with path), it will call set_mo_source() to update the data file automatically.
                    
+
                    Reading an Excel file (.xlsx) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by get_mo_source() in only a couple of microseconds (a millionth of a second).
                    How it works
                    
 
-    
 
-
                    Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:
                    -  |         A          |      B      |
+
                    Imagine this data on a sheet of an Excel file (mo codes were looked up in the microorganisms data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:
                      |         A          |      B      |
 --|--------------------|-------------|
 1 | Organisation XYZ   | mo          |
 2 | lab_mo_ecoli       | B_ESCHR_COL |
@@ -271,49 +268,45 @@ This is the fastest way to have your organisation (or analysis) specific codes p
 4 |                    |             |
 
                    
 
-
                    We save it as 'home/me/ourcodes.xlsx'. Now we have to set it as a source:
                    -set_mo_source("home/me/ourcodes.xlsx")
+
                    We save it as "home/me/ourcodes.xlsx". Now we have to set it as a source:
                    set_mo_source("home/me/ourcodes.xlsx")
 # Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
 
                    
 
-
                    It has now created a file "~/.mo_source.rds" with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept.
                    
-
                    And now we can use it in our functions:
                    -as.mo("lab_mo_ecoli")
-[1] B_ESCHR_COL
+
                    It has now created a file "~/.mo_source.rds" with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept.
                    
+
                    And now we can use it in our functions:
                    as.mo("lab_mo_ecoli")
+\[1\] B_ESCHR_COLI
 
 mo_genus("lab_mo_kpneumoniae")
 [1] "Klebsiella"
 
 # other input values still work too
 as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
-[1] B_ESCHR_COL  B_ESCHR_COL  B_ESCHR_COL
+[1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
 
                    
 
-
                    If we edit the Excel file to, let's say, this:
                    -  |         A          |      B      |
---|--------------------|-------------|
-1 | Organisation XYZ   | mo          |
-2 | lab_mo_ecoli       | B_ESCHR_COL |
-3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
-4 | lab_Staph_aureus   | B_STPHY_AUR |
-5 |                    |             |
+
                    If we edit the Excel file to, let's say, this:
                      |         A          |       B      |
+--|--------------------|--------------|
+1 | Organisation XYZ   | mo           |
+2 | lab_mo_ecoli       | B_ESCHR_COLI |
+3 | lab_mo_kpneumoniae | B_KLBSL_PNMN |
+4 | lab_Staph_aureus   | B_STPHY_AURS |
+5 |                    |              |
 
                    
 
-
                    ...any new usage of an MO function in this package will update your data:
                    -as.mo("lab_mo_ecoli")
+
                    ...any new usage of an MO function in this package will update your data:
                    as.mo("lab_mo_ecoli")
 # Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
-[1] B_ESCHR_COL
+[1] B_ESCHR_COLI
 
 mo_genus("lab_Staph_aureus")
 [1] "Staphylococcus"
 
                    
 
-
                    To remove the reference completely, just use any of these:
                    -set_mo_source("")
+
                    To remove the reference completely, just use any of these:
                    set_mo_source("")
 set_mo_source(NULL)
 # Removed mo_source file '~/.mo_source.rds'.
 
                    
 
+
     
                    Read more on our website!
                    
 
     
@@ -326,7 +319,6 @@ set_mo_source(NULL)
     
 
diff --git a/docs/reference/p_symbol.html b/docs/reference/p_symbol.html
index 36779c4d4..fbbd17ad8 100644
--- a/docs/reference/p_symbol.html
+++ b/docs/reference/p_symbol.html
@@ -6,7 +6,7 @@
 
 
 
-Symbol of a p value — p_symbol • AMR (for R)
+Symbol of a p-value — p_symbol • AMR (for R)
 
 
 
@@ -50,8 +50,8 @@
   
   
 
-
-
+
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -228,13 +228,13 @@
 
                    
   
                    
     
                    
-    
                    Symbol of a p value
                    
+    
                    Symbol of a p-value
                    
     
     
     
                    
 
     
                    
-    
                    Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above p = 1 will return NA.
                    
+    
                    Return the symbol related to the p-value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above p = 1 will return NA.
                    
     
                    
 
     
                    p_symbol(p, emptychar = " ")
                    
diff --git a/docs/reference/proportion.html b/docs/reference/proportion.html
index 9e2d044b3..e16e1f162 100644
--- a/docs/reference/proportion.html
+++ b/docs/reference/proportion.html
@@ -51,7 +51,7 @@
   
 
 
-
 
 
@@ -86,7 +86,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
                    
 
@@ -235,45 +235,50 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
                    
 
     
                    
-    
                    These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
                    
+    
                    These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyr::summarise() and support grouped variables, please see Examples.
                    
 
                    resistance() should be used to calculate resistance, susceptibility() should be used to calculate susceptibility.
                    
     
                    
 
-    
                    resistance(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+    
                    resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-susceptibility(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_R(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_IR(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_I(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_SI(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_S(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_df(data, translate_ab = "name", language = get_locale(),
-  minimum = 30, as_percent = FALSE, combine_SI = TRUE,
-  combine_IR = FALSE)
+proportion_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  minimum = 30,
+  as_percent = FALSE,
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
 
-rsi_df(data, translate_ab = "name", language = get_locale(),
-  minimum = 30, as_percent = FALSE, combine_SI = TRUE,
-  combine_IR = FALSE)
                    
+rsi_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  minimum = 30,
+  as_percent = FALSE,
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
                    
 
     
                    Arguments
                    
     
-      
+      
@@ -285,19 +290,19 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
-      
+      
-      
+      
-      
+      
-      
+      
@@ -314,20 +319,18 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
                    M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
                    Value
                    
 
-    
                    Double or, when as_percent = TRUE, a character.
                    
+    
                    A double or, when as_percent = TRUE, a character.
                    Details
                    
 
-    
                    The function resistance() is equal to the function proportion_R(). The function susceptibility() is equal to the function proportion_SI().
                     
-
                    Remember that you should filter your table to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate to determine them in your data set.
                    
-
                    These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the count functions to count isolates. The function susceptibility() is essentially equal to count_susceptible() / count_all(). Low counts can infuence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum parameter).
                    
-
                    The function proportion_df() takes any variable from data that has an "rsi" class (created with as.rsi()) and calculates the proportions R, I and S. The function rsi_df() works exactly like proportion_df(), but adds the number of isolates.
                    
+    
                    The function resistance() is equal to the function proportion_R(). The function susceptibility() is equal to the function proportion_SI().
                    
+
                    Remember that you should filter your table to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set.
                    
+
                    These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the AMR::count() functions to count isolates. The function susceptibility() is essentially equal to count_susceptible() / count_all(). Low counts can infuence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum parameter).
                    
+
                    The function proportion_df() takes any variable from data that has an rsi class (created with as.rsi()) and calculates the proportions R, I and S. The function rsi_df() works exactly like proportion_df(), but adds the number of isolates.
                    Combination therapy
                    
 
     
 
-
                    When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how susceptibility works to calculate the %SI:
                    
-
                    ---------------------------------------------------------------------
+
                    When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how susceptibility() works to calculate the %SI:
                    --------------------------------------------------------------------
                     only_all_tested = FALSE  only_all_tested = TRUE
                     -----------------------  -----------------------
  Drug A    Drug B   include as  include as   include as  include as
@@ -345,11 +348,11 @@ resistance() should be used to calculate resistance, susceptibility() should be
 --------------------------------------------------------------------
 
                    
 
-
                    Please note that, in combination therapies, for only_all_tested = TRUE applies that:
                    -    count_S()    +   count_I()    +   count_R()    = count_all()
+
                    Please note that, in combination therapies, for only_all_tested = TRUE applies that:
                        count_S()    +   count_I()    +   count_R()    = count_all()
   proportion_S() + proportion_I() + proportion_R() = 1
-
                    and that, in combination therapies, for only_all_tested = FALSE applies that:
                    -    count_S()    +   count_I()    +   count_R()    >= count_all()
+
                    
+
+
                    and that, in combination therapies, for only_all_tested = FALSE applies that:
                        count_S()    +   count_I()    +   count_R()    >= count_all()
   proportion_S() + proportion_I() + proportion_R() >= 1
 
                    
 
@@ -358,15 +361,14 @@ resistance() should be used to calculate resistance, susceptibility() should be
 
     
 
-
                    In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                    
-
                      
+
                      In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                        
 
                      • S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
                        • 
 
                      • I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
                        • 
 
                      • R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
                        • 
 
                      
 
 
                      Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
                      
-
                      This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                      
+
                      This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                      
     
                      Read more on our website!
                      
 
     
@@ -374,7 +376,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
 
                      On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                      
     
                      See also
                      
 
-    
                      count_* to count resistant and susceptible isolates.
                      
+    
                      AMR::count() to count resistant and susceptible isolates.
                      
 
     
                      Examples
                      
     
                      # example_isolates is a data set available in the AMR package.
diff --git a/docs/reference/read.4D.html b/docs/reference/read.4D.html
index 84db8e425..60c43fa12 100644
--- a/docs/reference/read.4D.html
+++ b/docs/reference/read.4D.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -234,14 +234,27 @@
     
 
     
                      
-    
                      This function is only useful for the MMB department of the UMCG. Use this function to import data by just defining the file parameter. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with as.mo and transform all antimicrobial columns with as.rsi.
                      
+    
                      This function is only useful for the MMB department of the UMCG. Use this function to import data by just defining the file parameter. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with as.mo() and transform all antimicrobial columns with as.rsi().
                      
     
                      
 
-    
                      read.4D(file, info = interactive(), header = TRUE, row.names = NULL,
-  sep = "\t", quote = "\"'", dec = ",", na.strings = c("NA", "",
-  "."), skip = 2, check.names = TRUE, strip.white = TRUE,
-  fill = TRUE, blank.lines.skip = TRUE, stringsAsFactors = FALSE,
-  fileEncoding = "UTF-8", encoding = "UTF-8")
                      
+    
                      read.4D(
+  file,
+  info = interactive(),
+  header = TRUE,
+  row.names = NULL,
+  sep = "\t",
+  quote = "\"'",
+  dec = ",",
+  na.strings = c("NA", "", "."),
+  skip = 2,
+  check.names = TRUE,
+  strip.white = TRUE,
+  fill = TRUE,
+  blank.lines.skip = TRUE,
+  stringsAsFactors = FALSE,
+  fileEncoding = "UTF-8",
+  encoding = "UTF-8"
+)
                      
 
     
                      Arguments
                      
     
                    
     
     
                    
       ...
                    one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
                    one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi() if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
                    		
     
                    
     
                    
       minimum
                    
     
                    
       only_all_tested
                    (for combination therapies, i.e. using more than one variable for ...) a logical to indicate that isolates must be tested for all antibiotics, see section Combination therapy below
                    (for combination therapies, i.e. using more than one variable for ...): a logical to indicate that isolates must be tested for all antibiotics, see section Combination therapy below
                    		
     
                    
     
                    
       data
                    a data.frame containing columns with class rsi (see as.rsi)
                    a data.frame containing columns with class rsi (see as.rsi())
                    		
     
                    
     
                    
       translate_ab
                    a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property
                    a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property()
                    		
     
                    
     
                    
       language
                    language of the returned text, defaults to system language (see get_locale) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
                    language of the returned text, defaults to system language (see get_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
                    		
     
                    
     
                    
       combine_SI
     
     
     
                    
diff --git a/docs/reference/resistance_predict.html b/docs/reference/resistance_predict.html
index 37ca38268..161340a5f 100644
--- a/docs/reference/resistance_predict.html
+++ b/docs/reference/resistance_predict.html
@@ -85,7 +85,7 @@
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
 
@@ -234,34 +234,59 @@
     
                    
-    
                    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.
                    
+    
                    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.
                    
     
                    
 
-    
                    resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,
-  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
-  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
+    
                    resistance_predict(
+  x,
+  col_ab,
+  col_date = NULL,
+  year_min = NULL,
+  year_max = NULL,
+  year_every = 1,
+  minimum = 30,
+  model = NULL,
+  I_as_S = TRUE,
+  preserve_measurements = TRUE,
+  info = TRUE,
+  ...
+)
 
-rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
-  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
-  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
+rsi_predict(
+  x,
+  col_ab,
+  col_date = NULL,
+  year_min = NULL,
+  year_max = NULL,
+  year_every = 1,
+  minimum = 30,
+  model = NULL,
+  I_as_S = TRUE,
+  preserve_measurements = TRUE,
+  info = TRUE,
+  ...
+)
 
 # S3 method for resistance_predict
-plot(x,
-  main = paste("Resistance Prediction of", x_name), ...)
+plot(x, main = paste("Resistance Prediction of", x_name), ...)
 
-ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
-  ribbon = TRUE, ...)
                    
+ggplot_rsi_predict(
+  x,
+  main = paste("Resistance Prediction of", x_name),
+  ribbon = TRUE,
+  ...
+)
                    Arguments
                    
       
     
 
     
 
     
     
                    
-      
+      
-      
+      
@@ -285,11 +310,11 @@
     
-      
+      
-      
+      
@@ -297,7 +322,7 @@
     
-      
+      
@@ -315,18 +340,20 @@
 
     
                    Value
                    
 
-    
                    data.frame with extra class "resistance_predict" with columns:
                      
+    
                      A data.frame with extra class resistance_predict with columns:
                        
 
                      • year
                        • 
 
                      • value, the same as estimated when preserve_measurements = FALSE, and a combination of observed and estimated otherwise
                        • 
 
                      • se_min, the lower bound of the standard error with a minimum of 0 (so the standard error will never go below 0%)
                        • 
 
                      • se_max the upper bound of the standard error with a maximum of 1 (so the standard error will never go above 100%)
                        • 
-
                      • observations, the total number of available observations in that year, i.e. S + I + R
                        • 
+
                      • observations, the total number of available observations in that year, i.e. \(S + I + R\)
                        • 
 
                      • observed, the original observed resistant percentages
                        • 
 
                      • estimated, the estimated resistant percentages, calculated by the model
                        • 
-
                      Furthermore, the model itself is available as an attribute: attributes(x)$model, see Examples.
                      
+
                    
+
+
                    Furthermore, the model itself is available as an attribute: attributes(x)$model, please see Examples.
                    Details
                    
 
-    
                    Valid options for the statistical model are:
                      
+    
                      Valid options for the statistical model (parameter model) are:
                        
 
                      • "binomial" or "binom" or "logit": a generalised linear regression model with binomial distribution
                        • 
 
                      • "loglin" or "poisson": a generalised log-linear regression model with poisson distribution
                        • 
 
                      • "lin" or "linear": a linear regression model
                        • 
@@ -336,15 +363,14 @@
 
     
 
-
                        In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                        
-
                          
+
                          In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
                            
 
                          • S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
                            • 
 
                          • I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
                            • 
 
                          • R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
                            • 
 
                          
 
 
                          Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
                          
-
                          This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                          
+
                          This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
                          
     
                          Read more on our website!
                          
 
     
@@ -352,10 +378,14 @@
 
                          On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                          
     
                          See also
                          
 
-    
                          The portion function to calculate resistance, 
                           lm glm
                          
+    
                          The proportion() functions to calculate resistance
                          
+
                          Models: lm() glm()
                          
 
     
                          Examples
                          
-    
                          x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial")
+    
                          x <- resistance_predict(example_isolates,
+                        col_ab = "AMX",
+                        year_min = 2010,
+                        model = "binomial")
 plot(x)
 ggplot_rsi_predict(x)
 
@@ -394,9 +424,9 @@
     scale_y_continuous(limits = c(0, 1),
                        breaks = seq(0, 1, 0.1),
                        labels = paste0(seq(0, 100, 10), "%")) +
-    labs(title = expression(paste("Forecast of amoxicillin resistance in ",
+    labs(title = expression(paste("Forecast of Amoxicillin Resistance in ",
                                   italic("E. coli"))),
-         y = "%IR",
+         y = "%R",
          x = "Year") +
     theme_minimal(base_size = 13)
 }
                          
diff --git a/docs/reference/rsi_translation.html b/docs/reference/rsi_translation.html
index 54166eb82..4d5776f8e 100644
--- a/docs/reference/rsi_translation.html
+++ b/docs/reference/rsi_translation.html
@@ -51,7 +51,7 @@
   
 
 
-
+
 
 
 
@@ -85,7 +85,7 @@
       
       
         AMR (for R)
-        0.8.0.9033
+        0.8.0.9036
       
     
 
@@ -234,7 +234,7 @@
     
 
     
                          
-    
                          Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use as.rsi to transform MICs or disks measurements to RSI values.
                          
+    
                          Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use as.rsi() to transform MICs or disks measurements to RSI values.
                          
     
                          
 
     
                          rsi_translation
                          
@@ -242,18 +242,17 @@
 
     
                          Format
                          
 
-    
                          A data.frame with 13,975 observations and 9 variables:
                          
-  
                          guideline
                          Name of the guideline
                          
-  
                          method
                          Either "MIC" or "DISK"
                          
-  
                          site
                          Body site, e.g. "Oral" or "Respiratory"
                          
-  
                          mo
                          Microbial ID, see as.mo
                          
-  
                          ab
                          Antibiotic ID, see as.ab
                          
-  
                          ref_tbl
                          Info about where the guideline rule can be found
                          
-  
                          disk_dose
                          Dose of the used disk diffusion method
                          
-  
                          breakpoint_S
                          Lowest MIC value or highest number of millimeters that leads to "S"
                          
-  
                          breakpoint_R
                          Highest MIC value or lowest number of millimeters that leads to "R"
                          
-
-
                          
+    
                          A data.frame with 13,975 observations and 9 variables:
                            
+
                          • guideline
                             Name of the guideline
                            • 
+
                          • method
                             Either "MIC" or "DISK"
                            • 
+
                          • site
                             Body site, e.g. "Oral" or "Respiratory"
                            • 
+
                          • mo
                             Microbial ID, see as.mo()
                            • 
+
                          • ab
                             Antibiotic ID, see as.ab()
                            • 
+
                          • ref_tbl
                             Info about where the guideline rule can be found
                            • 
+
                          • disk_dose
                             Dose of the used disk diffusion method
                            • 
+
                          • breakpoint_S
                             Lowest MIC value or highest number of millimeters that leads to "S"
                            • 
+
                          • breakpoint_R
                             Highest MIC value or lowest number of millimeters that leads to "R"
                            • 
+
                          
     
                          Read more on our website!
                          
 
     
diff --git a/docs/reference/skewness.html b/docs/reference/skewness.html
index a5841cb64..fec2d6608 100644
--- a/docs/reference/skewness.html
+++ b/docs/reference/skewness.html
@@ -86,7 +86,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
       
       
         AMR (for R)
-        0.8.0.9032
+        0.8.0.9036
       
     
 
@@ -255,7 +255,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
     
                    
-      
+      
@@ -270,7 +270,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr
 
                    On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
                    See also
                    
 
-    
                    kurtosis
                    
+    
                    kurtosis()
                    
 
@@ -234,7 +234,7 @@
     
                    
-    
                    For language-dependent output of AMR functions, like mo_name,  mo_type and ab_name.
                    
+    
                    For language-dependent output of AMR functions, like mo_name(), mo_type() and ab_name().
                    
     
                    get_locale()
                    
@@ -245,8 +245,8 @@
     
                    Strings will be translated to foreign languages if they are defined in a local translation file. Additions to this file can be suggested at our repository. The file can be found here: https://gitlab.com/msberends/AMR/blob/master/data-raw/translations.tsv.
                    Currently supported languages can be found if running: unique(AMR:::translations_file$lang).
                    Please suggest your own translations by creating a new issue on our repository.
                    
-
                    This file will be read by all functions where a translated output can be desired, like all mo_property functions (mo_fullname, mo_type, etc.).
                    
-
                    The system language will be used at default, if that language is supported. The system language can be overwritten with getOption("AMR_locale").
                    
+
                    This file will be read by all functions where a translated output can be desired, like all mo_property() functions (mo_fullname(), mo_type(), etc.).
                    
+
                    The system language will be used at default, if that language is supported. The system language can be overwritten with Sys.setenv(AMR_locale = yourlanguage).
                    Read more on our website!
                    
 
     
diff --git a/man/AMR-deprecated.Rd b/man/AMR-deprecated.Rd
index 1210eac93..9b8fc1059 100644
--- a/man/AMR-deprecated.Rd
+++ b/man/AMR-deprecated.Rd
@@ -8,6 +8,7 @@
 \alias{portion_I}
 \alias{portion_SI}
 \alias{portion_S}
+\alias{portion_df}
 \title{Deprecated functions}
 \usage{
 p.symbol(...)
@@ -21,6 +22,8 @@ portion_I(...)
 portion_SI(...)
 
 portion_S(...)
+
+portion_df(...)
 }
 \description{
 These functions are so-called '\link{Deprecated}'. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by (if there is one).
diff --git a/man/AMR.Rd b/man/AMR.Rd
index 6c834394f..18b6ef1a8 100644
--- a/man/AMR.Rd
+++ b/man/AMR.Rd
@@ -13,17 +13,17 @@ We created this package for both academic research and routine analysis at the F
 
 This package can be used for:
 \itemize{
-  \item{Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life}
-  \item{Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines}
-  \item{Determining first isolates to be used for AMR analysis}
-  \item{Calculating antimicrobial resistance}
-  \item{Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)}
-  \item{Calculating (empirical) susceptibility of both mono therapy and combination therapies}
-  \item{Predicting future antimicrobial resistance using regression models}
-  \item{Getting properties for any microorganism (like Gram stain, species, genus or family)}
-  \item{Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)}
-  \item{Plotting antimicrobial resistance}
-  \item{Applying EUCAST expert rules}
+\item Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the \href{http://www.catalogueoflife.org}{Catalogue of Life}
+\item Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
+\item Determining first isolates to be used for AMR analysis
+\item Calculating antimicrobial resistance
+\item Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO)
+\item Calculating (empirical) susceptibility of both mono therapy and combination therapies
+\item Predicting future antimicrobial resistance using regression models
+\item Getting properties for any microorganism (like Gram stain, species, genus or family)
+\item Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name)
+\item Plotting antimicrobial resistance
+\item Applying EUCAST expert rules
 }
 }
 \section{Read more on our website!}{
@@ -36,11 +36,11 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 For suggestions, comments or questions, please contact us at:
 
 Matthijs S. Berends \cr
-m.s.berends [at] umcg [dot] nl \cr
+m.s.berends \link{at} umcg \link{dot} nl \cr
 Department of Medical Microbiology, University of Groningen \cr
 University Medical Center Groningen \cr
 Post Office Box 30001 \cr
-9700 RB Groningen
+9700 RB Groningen \cr
 The Netherlands
 
 If you have found a bug, please file a new issue at: \cr
diff --git a/man/WHOCC.Rd b/man/WHOCC.Rd
index c99c8ec14..3ec5c0bd1 100644
--- a/man/WHOCC.Rd
+++ b/man/WHOCC.Rd
@@ -9,13 +9,13 @@ All antimicrobial drugs and their official names, ATC codes, ATC groups and defi
 \section{WHOCC}{
 
 \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
-This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). 
+This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
 
 These have become the gold standard for international drug utilisation monitoring and research.
 
 The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
 
-\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
+\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package.} See \url{https://www.whocc.no/copyright_disclaimer/.}
 }
 
 \section{Read more on our website!}{
diff --git a/man/WHONET.Rd b/man/WHONET.Rd
index ef861befb..a320993bf 100644
--- a/man/WHONET.Rd
+++ b/man/WHONET.Rd
@@ -5,39 +5,39 @@
 \alias{WHONET}
 \title{Data set with 500 isolates - WHONET example}
 \format{A \code{\link{data.frame}} with 500 observations and 53 variables:
-\describe{
-  \item{\code{Identification number}}{ID of the sample}
-  \item{\code{Specimen number}}{ID of the specimen}
-  \item{\code{Organism}}{Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using \code{\link{as.mo}}.}
-  \item{\code{Country}}{Country of origin}
-  \item{\code{Laboratory}}{Name of laboratory}
-  \item{\code{Last name}}{Last name of patient}
-  \item{\code{First name}}{Initial of patient}
-  \item{\code{Sex}}{Gender of patient}
-  \item{\code{Age}}{Age of patient}
-  \item{\code{Age category}}{Age group, can also be looked up using \code{\link{age_groups}}}
-  \item{\code{Date of admission}}{Date of hospital admission}
-  \item{\code{Specimen date}}{Date when specimen was received at laboratory}
-  \item{\code{Specimen type}}{Specimen type or group}
-  \item{\code{Specimen type (Numeric)}}{Translation of \code{"Specimen type"}}
-  \item{\code{Reason}}{Reason of request with Differential Diagnosis}
-  \item{\code{Isolate number}}{ID of isolate}
-  \item{\code{Organism type}}{Type of microorganism, can also be looked up using \code{\link{mo_type}}}
-  \item{\code{Serotype}}{Serotype of microorganism}
-  \item{\code{Beta-lactamase}}{Microorganism produces beta-lactamase?}
-  \item{\code{ESBL}}{Microorganism produces extended spectrum beta-lactamase?}
-  \item{\code{Carbapenemase}}{Microorganism produces carbapenemase?}
-  \item{\code{MRSA screening test}}{Microorganism is possible MRSA?}
-  \item{\code{Inducible clindamycin resistance}}{Clindamycin can be induced?}
-  \item{\code{Comment}}{Other comments}
-  \item{\code{Date of data entry}}{Date this data was entered in WHONET}
-  \item{\code{AMP_ND10:CIP_EE}}{27 different antibiotics. You can lookup the abbreviatons in the \code{\link{antibiotics}} data set, or use e.g. \code{\link{ab_name}("AMP")} to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using \code{\link{as.rsi}}.}
+\itemize{
+\item \verb{Identification number}\cr ID of the sample
+\item \verb{Specimen number}\cr ID of the specimen
+\item \code{Organism}\cr Name of the microorganism. Before analysis, you should transform this to a valid microbial class, using \code{\link[=as.mo]{as.mo()}}.
+\item \code{Country}\cr Country of origin
+\item \code{Laboratory}\cr Name of laboratory
+\item \verb{Last name}\cr Last name of patient
+\item \verb{First name}\cr Initial of patient
+\item \code{Sex}\cr Gender of patient
+\item \code{Age}\cr Age of patient
+\item \verb{Age category}\cr Age group, can also be looked up using \code{\link[=age_groups]{age_groups()}}
+\item \verb{Date of admission}\cr Date of hospital admission
+\item \verb{Specimen date}\cr Date when specimen was received at laboratory
+\item \verb{Specimen type}\cr Specimen type or group
+\item \verb{Specimen type (Numeric)}\cr Translation of \code{"Specimen type"}
+\item \code{Reason}\cr Reason of request with Differential Diagnosis
+\item \verb{Isolate number}\cr ID of isolate
+\item \verb{Organism type}\cr Type of microorganism, can also be looked up using \code{\link[=mo_type]{mo_type()}}
+\item \code{Serotype}\cr Serotype of microorganism
+\item \code{Beta-lactamase}\cr Microorganism produces beta-lactamase?
+\item \code{ESBL}\cr Microorganism produces extended spectrum beta-lactamase?
+\item \code{Carbapenemase}\cr Microorganism produces carbapenemase?
+\item \verb{MRSA screening test}\cr Microorganism is possible MRSA?
+\item \verb{Inducible clindamycin resistance}\cr Clindamycin can be induced?
+\item \code{Comment}\cr Other comments
+\item \verb{Date of data entry}\cr Date this data was entered in WHONET
+\item \code{AMP_ND10:CIP_EE}\cr 27 different antibiotics. You can lookup the abbreviatons in the \link{antibiotics} data set, or use e.g. \code{\link[=ab_name]{ab_name("AMP")}} to get the official name immediately. Before analysis, you should transform this to a valid antibiotic class, using \code{\link[=as.rsi]{as.rsi()}}.
 }}
 \usage{
 WHONET
 }
 \description{
-This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our \code{\link{example_isolates}} data set.
+This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The data itself was based on our \link{example_isolates} data set.
 }
 \section{Read more on our website!}{
 
diff --git a/man/ab_property.Rd b/man/ab_property.Rd
index af3bb84e9..eb366109d 100644
--- a/man/ab_property.Rd
+++ b/man/ab_property.Rd
@@ -38,30 +38,30 @@ ab_info(x, language = get_locale(), ...)
 ab_property(x, property = "name", language = get_locale(), ...)
 }
 \arguments{
-\item{x}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.ab}}}
+\item{x}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link[=as.ab]{as.ab()}}}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
 \item{tolower}{logical to indicate whether the first character of every output should be transformed to a lower case character. This will lead to e.g. "polymyxin B" and not "polymyxin b".}
 
-\item{...}{other parameters passed on to \code{\link{as.ab}}}
+\item{...}{other parameters passed on to \code{\link[=as.ab]{as.ab()}}}
 
 \item{administration}{way of administration, either \code{"oral"} or \code{"iv"}}
 
 \item{units}{a logical to indicate whether the units instead of the DDDs itself must be returned, see Examples}
 
-\item{property}{one of the column names of one of the \code{\link{antibiotics}} data set}
+\item{property}{one of the column names of one of the \link{antibiotics} data set}
 }
 \value{
 \itemize{
-  \item{An \code{integer} in case of \code{ab_cid}}
-  \item{A named \code{list} in case of \code{ab_info} and multiple \code{ab_synonyms}/\code{ab_tradenames}}
-  \item{A \code{double} in case of \code{ab_ddd}}
-  \item{A \code{character} in all other cases}
+\item An \code{\link{integer}} in case of \code{\link[=ab_cid]{ab_cid()}}
+\item A named \code{\link{list}} in case of \code{\link[=ab_info]{ab_info()}} and multiple \code{\link[=ab_synonyms]{ab_synonyms()}}/\code{\link[=ab_tradenames]{ab_tradenames()}}
+\item A \code{\link{double}} in case of \code{\link[=ab_ddd]{ab_ddd()}}
+\item A \code{\link{character}} in all other cases
 }
 }
 \description{
-Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set. All input values will be evaluated internally with \code{\link{as.ab}}.
+Use these functions to return a specific property of an antibiotic from the \link{antibiotics} data set. All input values will be evaluated internally with \code{\link[=as.ab]{as.ab()}}.
 }
 \details{
 All output will be \link{translate}d where possible.
@@ -112,5 +112,5 @@ ab_name(21319)       # "Flucloxacillin" (using CID)
 ab_name("J01CF05")   # "Flucloxacillin" (using ATC)
 }
 \seealso{
-\code{\link{antibiotics}}
+\link{antibiotics}
 }
diff --git a/man/age.Rd b/man/age.Rd
index e68e56f3b..bf8ffa5d3 100644
--- a/man/age.Rd
+++ b/man/age.Rd
@@ -7,11 +7,11 @@
 age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE)
 }
 \arguments{
-\item{x}{date(s), will be coerced with \code{\link{as.POSIXlt}}}
+\item{x}{date(s), will be coerced with \code{\link[=as.POSIXlt]{as.POSIXlt()}}}
 
-\item{reference}{reference date(s) (defaults to today), will be coerced with \code{\link{as.POSIXlt}} and cannot be lower than \code{x}}
+\item{reference}{reference date(s) (defaults to today), will be coerced with \code{\link[=as.POSIXlt]{as.POSIXlt()}} and cannot be lower than \code{x}}
 
-\item{exact}{a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of \href{https://en.wikipedia.org/wiki/Year-to-date}{year-to-date} (YTD) of \code{x} by the number of days in a year of \code{reference} (either 365 or 366).}
+\item{exact}{a logical to indicate whether age calculation should be exact, i.e. with decimals. It divides the number of days of \href{https://en.wikipedia.org/wiki/Year-to-date}{year-to-date} (YTD) of \code{x} by the number of days in the year of \code{reference} (either 365 or 366).}
 
 \item{na.rm}{a logical to indicate whether missing values should be removed}
 }
@@ -37,5 +37,5 @@ df$age_exact <- age(df$birth_date, exact = TRUE)
 df
 }
 \seealso{
-To split ages into groups, use the \code{\link{age_groups}} function.
+To split ages into groups, use the \code{\link[=age_groups]{age_groups()}} function.
 }
diff --git a/man/age_groups.Rd b/man/age_groups.Rd
index 8272e5780..793a3e310 100644
--- a/man/age_groups.Rd
+++ b/man/age_groups.Rd
@@ -7,7 +7,7 @@
 age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)
 }
 \arguments{
-\item{x}{age, e.g. calculated with \code{\link{age}}}
+\item{x}{age, e.g. calculated with \code{\link[=age]{age()}}}
 
 \item{split_at}{values to split \code{x} at, defaults to age groups 0-11, 12-24, 25-54, 55-74 and 75+. See Details.}
 
@@ -22,15 +22,15 @@ Split ages into age groups defined by the \code{split} parameter. This allows fo
 \details{
 To split ages, the input can be:
 \itemize{
-  \item{A numeric vector. A vector of e.g. \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+.
-        The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).}
-  \item{A character:}
-    \itemize{
-      \item{\code{"children"} or \code{"kids"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.}
-      \item{\code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85)}. This will split on 0-64, 65-74, 75-84, 85+.}
-      \item{\code{"fives"}, equivalent of: \code{1:20 * 5}. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.}
-      \item{\code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.}
-    }
+\item A numeric vector. A vector of e.g. \code{c(10, 20)} will split on 0-9, 10-19 and 20+. A value of only \code{50} will split on 0-49 and 50+.
+The default is to split on young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) and elderly (75+).
+\item A character:
+\itemize{
+\item \code{"children"} or \code{"kids"}, equivalent of: \code{c(0, 1, 2, 4, 6, 13, 18)}. This will split on 0, 1, 2-3, 4-5, 6-12, 13-17 and 18+.
+\item \code{"elderly"} or \code{"seniors"}, equivalent of: \code{c(65, 75, 85)}. This will split on 0-64, 65-74, 75-84, 85+.
+\item \code{"fives"}, equivalent of: \code{1:20 * 5}. This will split on 0-4, 5-9, 10-14, ..., 90-94, 95-99, 100+.
+\item \code{"tens"}, equivalent of: \code{1:10 * 10}. This will split on 0-9, 10-19, 20-29, ... 80-89, 90-99, 100+.
+}
 }
 }
 \section{Read more on our website!}{
@@ -72,5 +72,5 @@ example_isolates \%>\%
 }
 }
 \seealso{
-To determine ages, based on one or more reference dates, use the \code{\link{age}} function.
+To determine ages, based on one or more reference dates, use the \code{\link[=age]{age()}} function.
 }
diff --git a/man/antibiotics.Rd b/man/antibiotics.Rd
index 4a826fe43..4f1e12fad 100644
--- a/man/antibiotics.Rd
+++ b/man/antibiotics.Rd
@@ -5,34 +5,36 @@
 \alias{antibiotics}
 \alias{antivirals}
 \title{Data sets with ~550 antimicrobials}
-\format{\strong{For the \code{antibiotics} data set: a \code{\link{data.frame}} with 452 observations and 13 variables:}
-\describe{
-  \item{\code{ab}}{Antibiotic ID as used in this package (like \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available}
-  \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02}}
-  \item{\code{cid}}{Compound ID as found in PubChem}
-  \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
-  \item{\code{group}}{A short and concise group name, based on WHONET and WHOCC definitions}
-  \item{\code{atc_group1}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like \code{"Macrolides, lincosamides and streptogramins"}}
-  \item{\code{atc_group2}}{Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like \code{"Macrolides"}}
-  \item{\code{abbr}}{List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)}
-  \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
-  \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
-  \item{\code{oral_units}}{Units of \code{oral_ddd}}
-  \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
-  \item{\code{iv_units}}{Units of \code{iv_ddd}}
+\format{\subsection{For the \link{antibiotics} data set: a \code{\link{data.frame}} with 452 observations and 13 variables:}{
+\itemize{
+\item \code{ab}\cr Antibiotic ID as used in this package (like \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
+\item \code{atc}\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like \code{J01CR02}
+\item \code{cid}\cr Compound ID as found in PubChem
+\item \code{name}\cr Official name as used by WHONET/EARS-Net or the WHO
+\item \code{group}\cr A short and concise group name, based on WHONET and WHOCC definitions
+\item \code{atc_group1}\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like \code{"Macrolides, lincosamides and streptogramins"}
+\item \code{atc_group2}\cr Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like \code{"Macrolides"}
+\item \code{abbr}\cr List of abbreviations as used in many countries, also for antibiotic susceptibility testing (AST)
+\item \code{synonyms}\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
+\item \code{oral_ddd}\cr Defined Daily Dose (DDD), oral treatment
+\item \code{oral_units}\cr Units of \code{oral_ddd}
+\item \code{iv_ddd}\cr Defined Daily Dose (DDD), parenteral treatment
+\item \code{iv_units}\cr Units of \code{iv_ddd}
+}
 }
 
-\strong{For the \code{antivirals} data set: a \code{\link{data.frame}} with 102 observations and 9 variables:}
-\describe{
-  \item{\code{atc}}{ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC}
-  \item{\code{cid}}{Compound ID as found in PubChem}
-  \item{\code{name}}{Official name as used by WHONET/EARS-Net or the WHO}
-  \item{\code{atc_group}}{Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC}
-  \item{\code{synonyms}}{Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID}
-  \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
-  \item{\code{oral_units}}{Units of \code{oral_ddd}}
-  \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
-  \item{\code{iv_units}}{Units of \code{iv_ddd}}
+\subsection{For the \link{antivirals} data set: a \code{\link{data.frame}} with 102 observations and 9 variables:}{
+\itemize{
+\item \code{atc}\cr ATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC
+\item \code{cid}\cr Compound ID as found in PubChem
+\item \code{name}\cr Official name as used by WHONET/EARS-Net or the WHO
+\item \code{atc_group}\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC
+\item \code{synonyms}\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
+\item \code{oral_ddd}\cr Defined Daily Dose (DDD), oral treatment
+\item \code{oral_units}\cr Units of \code{oral_ddd}
+\item \code{iv_ddd}\cr Defined Daily Dose (DDD), parenteral treatment
+\item \code{iv_units}\cr Units of \code{iv_ddd}
+}
 }}
 \source{
 World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC): \url{https://www.whocc.no/atc_ddd_index/}
@@ -47,7 +49,7 @@ antibiotics
 antivirals
 }
 \description{
-Two data sets containing all antibiotics/antimycotics and antivirals. Use \code{\link{as.ab}} or one of the \code{\link{ab_property}} functions to retrieve values from the \code{antibiotics} data set. Three identifiers are included in this data set: an antibiotic ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
+Two data sets containing all antibiotics/antimycotics and antivirals. Use \code{\link[=as.ab]{as.ab()}} or one of the \code{\link[=ab_property]{ab_property()}} functions to retrieve values from the \link{antibiotics} data set. Three identifiers are included in this data set: an antibiotic ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes.
 }
 \details{
 Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}.
@@ -57,13 +59,13 @@ Synonyms (i.e. trade names) are derived from the Compound ID (\code{cid}) and co
 \section{WHOCC}{
 
 \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
-This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). 
+This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
 
 These have become the gold standard for international drug utilisation monitoring and research.
 
 The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
 
-\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
+\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package.} See \url{https://www.whocc.no/copyright_disclaimer/.}
 }
 
 \section{Read more on our website!}{
@@ -72,6 +74,6 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{microorganisms}}
+\link{microorganisms}
 }
 \keyword{datasets}
diff --git a/man/as.ab.Rd b/man/as.ab.Rd
index bfdf2c8ae..8ae403a73 100644
--- a/man/as.ab.Rd
+++ b/man/as.ab.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/ab.R
 \name{as.ab}
 \alias{as.ab}
+\alias{ab}
 \alias{is.ab}
 \title{Transform to antibiotic ID}
 \usage{
@@ -15,15 +16,15 @@ is.ab(x)
 \item{...}{arguments passed on to internal functions}
 }
 \value{
-Character (vector) with class \code{"ab"}. Unknown values will return \code{NA}.
+Character (vector) with class \code{\link{ab}}. Unknown values will return \code{NA}.
 }
 \description{
-Use this function to determine the antibiotic code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and synonyms (brand names).
+Use this function to determine the antibiotic code of one or more antibiotics. The data set \link{antibiotics} will be searched for abbreviations, official names and synonyms (brand names).
 }
 \details{
-All entries in the \code{\link{antibiotics}} data set have three different identifiers: a human readable EARS-Net code (column \code{ab}, used by ECDC and WHONET), an ATC code (column \code{atc}, used by WHO), and a CID code (column \code{cid}, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
+All entries in the \link{antibiotics} data set have three different identifiers: a human readable EARS-Net code (column \code{ab}, used by ECDC and WHONET), an ATC code (column \code{atc}, used by WHO), and a CID code (column \code{cid}, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem.
 
-Use the \code{\link{ab_property}} functions to get properties based on the returned antibiotic ID, see Examples.
+Use the \code{\link[=ab_property]{ab_property()}} functions to get properties based on the returned antibiotic ID, see Examples.
 }
 \section{Source}{
 
@@ -37,13 +38,13 @@ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{htt
 \section{WHOCC}{
 
 \if{html}{\figure{logo_who.png}{options: height=60px style=margin-bottom:5px} \cr}
-This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}). 
+This package contains \strong{all ~550 antibiotic, antimycotic and antiviral drugs} and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, \url{https://www.whocc.no}) and the Pharmaceuticals Community Register of the European Commission (\url{http://ec.europa.eu/health/documents/community-register/html/atc.htm}).
 
 These have become the gold standard for international drug utilisation monitoring and research.
 
 The WHOCC is located in Oslo at the Norwegian Institute of Public Health and funded by the Norwegian government. The European Commission is the executive of the European Union and promotes its general interest.
 
-\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See \url{https://www.whocc.no/copyright_disclaimer/}.}
+\strong{NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package.} See \url{https://www.whocc.no/copyright_disclaimer/.}
 }
 
 \section{Read more on our website!}{
@@ -70,5 +71,5 @@ ab_name("J01FA01")    # "Erythromycin"
 ab_name("eryt")       # "Erythromycin"
 }
 \seealso{
-\code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
+\link{antibiotics} for the dataframe that is being used to determine ATCs.
 }
diff --git a/man/as.disk.Rd b/man/as.disk.Rd
index ccbc1d3a3..54c2f3953 100644
--- a/man/as.disk.Rd
+++ b/man/as.disk.Rd
@@ -16,13 +16,13 @@ is.disk(x)
 \item{na.rm}{a logical indicating whether missing values should be removed}
 }
 \value{
-Ordered integer factor with new class \code{disk}
+Ordered integer factor with new class \code{\link{disk}}
 }
 \description{
-This transforms a vector to a new class \code{disk}, which is a growth zone size (around an antibiotic disk) in millimeters between 6 and 99.
+This transforms a vector to a new class \code{\link{disk}}, which is a growth zone size (around an antibiotic disk) in millimeters between 6 and 99.
 }
 \details{
-Interpret disk values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
+Interpret disk values as RSI values with \code{\link[=as.rsi]{as.rsi()}}. It supports guidelines from EUCAST and CLSI.
 }
 \section{Read more on our website!}{
 
@@ -41,5 +41,5 @@ as.rsi(x = 12,
        guideline = "CLSI")
 }
 \seealso{
-\code{\link{as.rsi}}
+\code{\link[=as.rsi]{as.rsi()}}
 }
diff --git a/man/as.mic.Rd b/man/as.mic.Rd
index 34e7acf5f..330574c0a 100755
--- a/man/as.mic.Rd
+++ b/man/as.mic.Rd
@@ -16,13 +16,13 @@ is.mic(x)
 \item{na.rm}{a logical indicating whether missing values should be removed}
 }
 \value{
-Ordered factor with new class \code{mic}
+Ordered \code{\link{factor}} with new class \code{\link{mic}}
 }
 \description{
-This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
+This transforms a vector to a new class \code{\link{mic}}, which is an ordered \code{\link{factor}} with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
 }
 \details{
-Interpret MIC values as RSI values with \code{\link{as.rsi}}. It supports guidelines from EUCAST and CLSI.
+To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST and CLSI.
 }
 \section{Read more on our website!}{
 
@@ -51,5 +51,5 @@ barplot(mic_data)
 freq(mic_data)
 }
 \seealso{
-\code{\link{as.rsi}}
+\code{\link[=as.rsi]{as.rsi()}}
 }
diff --git a/man/as.mo.Rd b/man/as.mo.Rd
index 85ffb00e5..9fe22a81d 100644
--- a/man/as.mo.Rd
+++ b/man/as.mo.Rd
@@ -10,8 +10,14 @@
 \alias{clear_mo_history}
 \title{Transform to microorganism ID}
 \usage{
-as.mo(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = TRUE,
-  reference_df = get_mo_source(), ...)
+as.mo(
+  x,
+  Becker = FALSE,
+  Lancefield = FALSE,
+  allow_uncertain = TRUE,
+  reference_df = get_mo_source(),
+  ...
+)
 
 is.mo(x)
 
@@ -24,33 +30,32 @@ mo_renamed()
 clear_mo_history(...)
 }
 \arguments{
-\item{x}{a character vector or a \code{data.frame} with one or two columns}
+\item{x}{a character vector or a \code{\link{data.frame}} with one or two columns}
 
-\item{Becker}{a logical to indicate whether \emph{Staphylococci} should be categorised into coagulase-negative \emph{Staphylococci} ("CoNS") and coagulase-positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1,2]. Note that this does not include species that were newly named after these publications, like \emph{S. caeli}.
+\item{Becker}{a logical to indicate whether \emph{Staphylococci} should be categorised into coagulase-negative \emph{Staphylococci} ("CoNS") and coagulase-positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} (1,2). Note that this does not include species that were newly named after these publications, like \emph{S. caeli}.
 
-  This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".}
+This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".}
 
-\item{Lancefield}{a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [3]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
+\item{Lancefield}{a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (3). These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
 
-  This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.}
+This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.}
 
-\item{allow_uncertain}{a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0) to indicate whether the input should be checked for less probable results, see Details}
+\item{allow_uncertain}{a number between \code{0} (or \code{"none"}) and \code{3} (or \code{"all"}), or \code{TRUE} (= \code{2}) or \code{FALSE} (= \code{0}) to indicate whether the input should be checked for less probable results, please see \emph{Details}}
 
-\item{reference_df}{a \code{data.frame} to use for extra reference when translating \code{x} to a valid \code{mo}. See \code{\link{set_mo_source}} and \code{\link{get_mo_source}} to automate the usage of your own codes (e.g. used in your analysis or organisation).}
+\item{reference_df}{a \code{\link{data.frame}} to use for extra reference when translating \code{x} to a valid \code{\link{mo}}. See \code{\link[=set_mo_source]{set_mo_source()}} and \code{\link[=get_mo_source]{get_mo_source()}} to automate the usage of your own codes (e.g. used in your analysis or organisation).}
 
 \item{...}{other parameters passed on to functions}
 }
 \value{
-Character (vector) with class \code{"mo"}
+A \code{\link{character}} vector with class \code{\link{mo}}
 }
 \description{
-Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. Please see Examples.
+Use this function to determine a valid microorganism ID (\code{\link{mo}}). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see Source). The input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. Please see \emph{Examples}.
 }
 \details{
-\strong{General info} \cr
-A microorganism ID from this package (class: \code{mo}) typically looks like these examples:\cr
-\preformatted{
-  Code               Full name
+\subsection{General info}{
+
+A microorganism ID from this package (class: \code{\link{mo}}) typically looks like these examples:\preformatted{  Code               Full name
   ---------------    --------------------------------------
   B_KLBSL            Klebsiella
   B_KLBSL_PNMN       Klebsiella pneumoniae
@@ -66,71 +71,75 @@ A microorganism ID from this package (class: \code{mo}) typically looks like the
 
 Values that cannot be coered will be considered 'unknown' and will get the MO code \code{UNKNOWN}.
 
-Use the \code{\link{mo_property}_*} functions to get properties based on the returned code, see Examples.
+Use the \code{\link[=mo_property]{mo_property_*}} functions to get properties based on the returned code, see Examples.
 
-The algorithm uses data from the Catalogue of Life (see below) and from one other source (see \code{\link{microorganisms}}).
+The algorithm uses data from the Catalogue of Life (see below) and from one other source (see \link{microorganisms}).
 
-The \code{as.mo()} function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
-\itemize{
-  \item{Human pathogenic prevalence: the function  starts with more prevalent microorganisms, followed by less prevalent ones;}
-  \item{Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;}
-  \item{Breakdown of input values to identify possible matches.}
+The \code{\link[=as.mo]{as.mo()}} function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
+\enumerate{
+\item Human pathogenic prevalence: the function  starts with more prevalent microorganisms, followed by less prevalent ones;
+\item Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;
+\item Breakdown of input values to identify possible matches.
 }
 
-This will lead to the effect that e.g. \code{"E. coli"} (a highly prevalent microorganism found in humans) will return the microbial ID of \emph{Escherichia coli} and not \emph{Entamoeba coli} (a less prevalent microorganism in humans), although the latter would alphabetically come first. 
+This will lead to the effect that e.g. \code{"E. coli"} (a highly prevalent microorganism found in humans) will return the microbial ID of \emph{Escherichia coli} and not \emph{Entamoeba coli} (a less prevalent microorganism in humans), although the latter would alphabetically come first.
+}
 
-\strong{Coping with uncertain results} \cr
-In addition, the \code{as.mo()} function can differentiate four levels of uncertainty to guess valid results: 
+\subsection{Coping with uncertain results}{
 
+In addition, the \code{\link[=as.mo]{as.mo()}} function can differentiate four levels of uncertainty to guess valid results:
 \itemize{
-  \item{Uncertainty level 0: no additional rules are applied;}
-  \item{Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;}
-  \item{Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;}
-  \item{Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.}
+\item Uncertainty level 0: no additional rules are applied;
+\item Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;
+\item Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
+\item Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.
 }
 
 This leads to e.g.:
-
 \itemize{
-  \item{\code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRPB}) needs review.}
-  \item{\code{"S. aureus - please mind: MRSA"}. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result \emph{Staphylococcus aureus} (\code{B_STPHY_AURS}) needs review.}
-  \item{\code{"Fluoroquinolone-resistant Neisseria gonorrhoeae"}. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result \emph{Neisseria gonorrhoeae} (\code{B_NESSR_GNRR}) needs review.}
+\item \code{"Streptococcus group B (known as S. agalactiae)"}. The text between brackets will be removed and a warning will be thrown that the result \emph{Streptococcus group B} (\code{B_STRPT_GRPB}) needs review.
+\item \code{"S. aureus - please mind: MRSA"}. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result \emph{Staphylococcus aureus} (\code{B_STPHY_AURS}) needs review.
+\item \code{"Fluoroquinolone-resistant Neisseria gonorrhoeae"}. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result \emph{Neisseria gonorrhoeae} (\code{B_NESSR_GNRR}) needs review.
 }
 
 The level of uncertainty can be set using the argument \code{allow_uncertain}. The default is \code{allow_uncertain = TRUE}, which is equal to uncertainty level 2. Using \code{allow_uncertain = FALSE} is equal to uncertainty level 0 and will skip all rules. You can also use e.g. \code{as.mo(..., allow_uncertain = 1)} to only allow up to level 1 uncertainty.
 
-There are three helper functions that can be run after then \code{as.mo()} function:
+There are three helper functions that can be run after then \code{\link[=as.mo]{as.mo()}} function:
 \itemize{
-  \item{Use \code{mo_uncertainties()} to get a \code{data.frame} with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \code{(n - 0.5 * L) / n}, where \emph{n} is the number of characters of the returned full name of the microorganism, and \emph{L} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} between that full name and the user input.}
-  \item{Use \code{mo_failures()} to get a vector with all values that could not be coerced to a valid value.}
-  \item{Use \code{mo_renamed()} to get a \code{data.frame} with all values that could be coerced based on an old, previously accepted taxonomic name.}
-}   
+\item Use \code{\link[=mo_uncertainties]{mo_uncertainties()}} to get a \code{\link{data.frame}} with all values that were coerced to a valid value, but with uncertainty. The output contains a score, that is calculated as \eqn{(n - 0.5 * L) / n}, where \emph{n} is the number of characters of the returned full name of the microorganism, and \emph{L} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} between that full name and the user input.
+\item Use \code{\link[=mo_failures]{mo_failures()}} to get a \code{\link{vector}} with all values that could not be coerced to a valid value.
+\item Use \code{\link[=mo_renamed]{mo_renamed()}} to get a \code{\link{data.frame}} with all values that could be coerced based on an old, previously accepted taxonomic name.
+}
+}
 
-\strong{Microbial prevalence of pathogens in humans} \cr
-The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \code{\link{microorganisms}} and \code{\link{microorganisms.old}} data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.
+\subsection{Microbial prevalence of pathogens in humans}{
 
-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 Enterobacteriales. 
+The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \link{microorganisms} and \link{microorganisms.old} data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.
 
-Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton} or \emph{Ureaplasma}. 
+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 Enterobacteriales.
+
+Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Aspergillus}, \emph{Bacteroides}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Chryseobacterium}, \emph{Cryptococcus}, \emph{Elisabethkingia}, \emph{Flavobacterium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Leptotrichia}, \emph{Mycoplasma}, \emph{Prevotella}, \emph{Rhodotorula}, \emph{Treponema}, \emph{Trichophyton} or \emph{Ureaplasma}.
 
 Group 3 (least prevalent microorganisms) consists of all other microorganisms.
+}
 
-\strong{Self-learning algorithm} \cr
-The \code{as.mo()} function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use \code{clear_mo_history()} to reset the algorithms. Only experience from your current \code{AMR} package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge.
+\subsection{Self-learning algorithm}{
+
+The \code{\link[=as.mo]{as.mo()}} function gains experience from previously determined microorganism IDs and learns from it. This drastically improves both speed and reliability. Use \code{\link[=clear_mo_history]{clear_mo_history()}} to reset the algorithms. Only experience from your current \code{AMR} package version is used. This is done because in the future the taxonomic tree (which is included in this package) may change for any organism and it consequently has to rebuild its knowledge.
 
 Usually, any guess after the first try runs 80-95\% faster than the first try.
 
 This resets with every update of this \code{AMR} package since results are saved to your local package library folder.
 }
+}
 \section{Source}{
 
-[1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
-
-[2] 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. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
-
-[3] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
-
-[4] 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}()}).
+\enumerate{
+\item Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
+\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. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
+\item Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/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()}}).
+}
 }
 
 \section{Catalogue of Life}{
@@ -138,7 +147,7 @@ This resets with every update of this \code{AMR} package since results are saved
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Read more on our website!}{
@@ -203,8 +212,9 @@ df <- df \%>\%
 }
 }
 \seealso{
-\code{\link{microorganisms}} for the \code{data.frame} that is being used to determine ID's. \cr
-The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code.
+\link{microorganisms} for the \code{\link{data.frame}} that is being used to determine ID's.
+
+The \code{\link[=mo_property]{mo_property()}} functions (like \code{\link[=mo_genus]{mo_genus()}}, \code{\link[=mo_gramstain]{mo_gramstain()}}) to get properties based on the returned code.
 }
 \keyword{Becker}
 \keyword{Lancefield}
diff --git a/man/as.rsi.Rd b/man/as.rsi.Rd
index b19246c33..85f30563f 100755
--- a/man/as.rsi.Rd
+++ b/man/as.rsi.Rd
@@ -16,54 +16,52 @@ as.rsi(x, ...)
 
 \method{as.rsi}{disk}(x, mo, ab, guideline = "EUCAST", ...)
 
-\method{as.rsi}{data.frame}(x, col_mo = NULL, guideline = "EUCAST",
-  ...)
+\method{as.rsi}{data.frame}(x, col_mo = NULL, guideline = "EUCAST", ...)
 
 is.rsi(x)
 
 is.rsi.eligible(x, threshold = 0.05)
 }
 \arguments{
-\item{x}{vector of values (for class \code{mic}: an MIC value in mg/L, for class \code{disk}: a disk diffusion radius in millimeters)}
+\item{x}{vector of values (for class \code{\link{mic}}: an MIC value in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimeters)}
 
 \item{...}{parameters passed on to methods}
 
-\item{mo}{a microorganism code, generated with \code{\link{as.mo}}}
+\item{mo}{a microorganism code, generated with \code{\link[=as.mo]{as.mo()}}}
 
-\item{ab}{an antimicrobial code, generated with \code{\link{as.ab}}}
+\item{ab}{an antimicrobial code, generated with \code{\link[=as.ab]{as.ab()}}}
 
 \item{guideline}{defaults to the latest included EUCAST guideline, run \code{unique(AMR::rsi_translation$guideline)} for all options}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
-\item{threshold}{maximum fraction of invalid antimicrobial interpretations of \code{x}, see Examples}
+\item{threshold}{maximum fraction of invalid antimicrobial interpretations of \code{x}, please see \emph{Examples}}
 }
 \value{
-Ordered factor with new class \code{rsi}
+Ordered factor with new class \code{\link{rsi}}
 }
 \description{
-Interpret MIC values according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
+Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class \code{\link{rsi}}, which is an ordered factor with levels \verb{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
 }
 \details{
 Run \code{unique(AMR::rsi_translation$guideline)} for a list of all supported guidelines.
 
-After using \code{as.rsi}, you can use \code{\link{eucast_rules}} to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
+After using \code{\link[=as.rsi]{as.rsi()}}, you can use \code{\link[=eucast_rules]{eucast_rules()}} to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
 
-The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
+The function \code{\link[=is.rsi.eligible]{is.rsi.eligible()}} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
 }
 \section{Interpretation of S, I and R}{
 
 In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
 \itemize{
-  \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
-  \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
-  \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
+\item \strong{S} - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+\item \strong{I} - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
+\item \strong{R} - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
 }
 
 Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
 
-This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates.
+This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
 }
 
 \section{Read more on our website!}{
@@ -109,5 +107,5 @@ is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
 is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
 }
 \seealso{
-\code{\link{as.mic}}
+\code{\link[=as.mic]{as.mic()}}
 }
diff --git a/man/atc_online.Rd b/man/atc_online.Rd
index b1af92937..4379cf48d 100644
--- a/man/atc_online.Rd
+++ b/man/atc_online.Rd
@@ -9,8 +9,12 @@
 \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
 }
 \usage{
-atc_online_property(atc_code, property, administration = "O",
-  url = "https://www.whocc.no/atc_ddd_index/?code=\%s&showdescription=no")
+atc_online_property(
+  atc_code,
+  property,
+  administration = "O",
+  url = "https://www.whocc.no/atc_ddd_index/?code=\%s&showdescription=no"
+)
 
 atc_online_groups(atc_code, ...)
 
@@ -23,38 +27,40 @@ atc_online_ddd(atc_code, ...)
 
 \item{administration}{type of administration when using \code{property = "Adm.R"}, see Details}
 
-\item{url}{url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.}
+\item{url}{url of website of the WHO. The sign \verb{\%s} can be used as a placeholder for ATC codes.}
 
 \item{...}{parameters to pass on to \code{atc_property}}
 }
 \description{
-Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \cr \strong{This function requires an internet connection.}
+Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit.
+
+\strong{This function requires an internet connection.}
 }
 \details{
 Options for parameter \code{administration}:
 \itemize{
-  \item{\code{"Implant"}}{ = Implant}
-  \item{\code{"Inhal"}}{ = Inhalation}
-  \item{\code{"Instill"}}{ = Instillation}
-  \item{\code{"N"}}{ = nasal}
-  \item{\code{"O"}}{ = oral}
-  \item{\code{"P"}}{ = parenteral}
-  \item{\code{"R"}}{ = rectal}
-  \item{\code{"SL"}}{ = sublingual/buccal}
-  \item{\code{"TD"}}{ = transdermal}
-  \item{\code{"V"}}{ = vaginal}
+\item \code{"Implant"} = Implant
+\item \code{"Inhal"} = Inhalation
+\item \code{"Instill"} = Instillation
+\item \code{"N"} = nasal
+\item \code{"O"} = oral
+\item \code{"P"} = parenteral
+\item \code{"R"} = rectal
+\item \code{"SL"} = sublingual/buccal
+\item \code{"TD"} = transdermal
+\item \code{"V"} = vaginal
 }
 
 Abbreviations of return values when using \code{property = "U"} (unit):
 \itemize{
-  \item{\code{"g"}}{ = gram}
-  \item{\code{"mg"}}{ = milligram}
-  \item{\code{"mcg"}}{ = microgram}
-  \item{\code{"U"}}{ = unit}
-  \item{\code{"TU"}}{ = thousand units}
-  \item{\code{"MU"}}{ = million units}
-  \item{\code{"mmol"}}{ = millimole}
-  \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
+\item \code{"g"} = gram
+\item \code{"mg"} = milligram
+\item `"mcg"`` = microgram
+\item \code{"U"} = unit
+\item \code{"TU"} = thousand units
+\item \code{"MU"} = million units
+\item \code{"mmol"} = millimole
+\item \code{"ml"} = milliliter (e.g. eyedrops)
 }
 }
 \section{Read more on our website!}{
diff --git a/man/availability.Rd b/man/availability.Rd
index 38202d10b..74bd416e1 100644
--- a/man/availability.Rd
+++ b/man/availability.Rd
@@ -7,18 +7,18 @@
 availability(tbl, width = NULL)
 }
 \arguments{
-\item{tbl}{a \code{data.frame} or \code{list}}
+\item{tbl}{a \code{\link{data.frame}} or \code{\link{list}}}
 
 \item{width}{number of characters to present the visual availability, defaults to filling the width of the console}
 }
 \value{
-\code{data.frame} with column names of \code{tbl} as row names
+\code{\link{data.frame}} with column names of \code{tbl} as row names
 }
 \description{
-Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. \code{\link{resistance}}.
+Easy check for availability of columns in a data set. This makes it easy to get an idea of which antimicrobial combination can be used for calculation with e.g. \code{\link[=resistance]{resistance()}}.
 }
 \details{
-The function returns a \code{data.frame} with columns \code{"resistant"} and \code{"visual_resistance"}. The values in that columns are calculated with \code{\link{resistance}}.
+The function returns a \code{\link{data.frame}} with columns \code{"resistant"} and \code{"visual_resistance"}. The values in that columns are calculated with \code{\link[=resistance]{resistance()}}.
 }
 \section{Read more on our website!}{
 
diff --git a/man/bug_drug_combinations.Rd b/man/bug_drug_combinations.Rd
index 64c83ebbe..30501d1b8 100644
--- a/man/bug_drug_combinations.Rd
+++ b/man/bug_drug_combinations.Rd
@@ -10,25 +10,32 @@
 \usage{
 bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
 
-\method{format}{bug_drug_combinations}(x,
-  translate_ab = "name (ab, atc)", language = get_locale(),
-  minimum = 30, combine_SI = TRUE, combine_IR = FALSE,
-  add_ab_group = TRUE, remove_intrinsic_resistant = FALSE,
-  decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark ==
-  ",", ".", ","), ...)
+\method{format}{bug_drug_combinations}(
+  x,
+  translate_ab = "name (ab, atc)",
+  language = get_locale(),
+  minimum = 30,
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  add_ab_group = TRUE,
+  remove_intrinsic_resistant = FALSE,
+  decimal.mark = getOption("OutDec"),
+  big.mark = ifelse(decimal.mark == ",", ".", ","),
+  ...
+)
 }
 \arguments{
 \item{x}{data with antibiotic columns, like e.g. \code{AMX} and \code{AMC}}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
-\item{FUN}{the function to call on the \code{mo} column to transform the microorganism IDs, defaults to \code{\link{mo_shortname}}}
+\item{FUN}{the function to call on the \code{mo} column to transform the microorganism IDs, defaults to \code{\link[=mo_shortname]{mo_shortname()}}}
 
 \item{...}{arguments passed on to \code{FUN}}
 
-\item{translate_ab}{a character of length 1 containing column names of the \code{\link{antibiotics}} data set}
+\item{translate_ab}{a character of length 1 containing column names of the \link{antibiotics} data set}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
 \item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.}
 
@@ -48,13 +55,13 @@ bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
     decimal point.}
 }
 \value{
-The function \code{bug_drug_combinations} returns a \code{data.frame} with columns "mo", "ab", "S", "I", "R" and "total".
+The function \code{\link[=bug_drug_combinations]{bug_drug_combinations()}} returns a \code{\link{data.frame}} with columns "mo", "ab", "S", "I", "R" and "total".
 }
 \description{
-Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{format} on the result to prettify it to a publicable/printable format, see Examples.
+Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use \code{\link[=format]{format()}} on the result to prettify it to a publicable/printable format, see Examples.
 }
 \details{
-The function \code{format} calculates the resistance per bug-drug combination. Use \code{combine_IR = FALSE} (default) to test R vs. S+I and \code{combine_IR = TRUE} to test R+I vs. S. 
+The function \code{\link[=format]{format()}} calculates the resistance per bug-drug combination. Use \code{combine_IR = FALSE} (default) to test R vs. S+I and \code{combine_IR = TRUE} to test R+I vs. S.
 
 The language of the output can be overwritten with \code{options(AMR_locale)}, please see \link{translate}.
 }
diff --git a/man/catalogue_of_life.Rd b/man/catalogue_of_life.Rd
index fa8bf2749..50cf7ae01 100644
--- a/man/catalogue_of_life.Rd
+++ b/man/catalogue_of_life.Rd
@@ -11,19 +11,19 @@ This package contains the complete taxonomic tree of almost all microorganisms f
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Included taxa}{
 
 Included are:
 \itemize{
-  \item{All ~61,000 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa}
-  \item{All ~8,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (like all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).}
-  \item{All ~150 (sub)species from ~100 other relevant genera from the kingdom of Animalia (like \emph{Strongyloides} and \emph{Taenia})}
-  \item{All ~23,000 previously accepted names of all included (sub)species (these were taxonomically renamed)}
-  \item{The complete taxonomic tree of all included (sub)species: from kingdom to subspecies}
-  \item{The responsible author(s) and year of scientific publication}
+\item All ~61,000 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa
+\item All ~8,500 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (like all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
+\item All ~150 (sub)species from ~100 other relevant genera from the kingdom of Animalia (like \emph{Strongyloides} and \emph{Taenia})
+\item All ~23,000 previously accepted names of all included (sub)species (these were taxonomically renamed)
+\item The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
+\item The responsible author(s) and year of scientific publication
 }
 
 The Catalogue of Life (\url{http://www.catalogueoflife.org}) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
@@ -60,13 +60,13 @@ mo_gramstain("E. coli") # based on kingdom and phylum, see ?mo_gramstain
 mo_ref("E. coli")
 # [1] "Castellani et al., 1919"
 
-# Do not get mistaken - the package only includes microorganisms
+# Do not get mistaken - this package is about microorganisms
 mo_kingdom("C. elegans")
 # [1] "Bacteria"                        # Bacteria?!
 mo_name("C. elegans")
 # [1] "Chroococcus limneticus elegans"  # Because a microorganism was found
 }
 \seealso{
-Data set \code{\link{microorganisms}} for the actual data. \cr
-Function \code{\link{as.mo}()} to use the data for intelligent determination of microorganisms.
+Data set \link{microorganisms} for the actual data. \cr
+Function \code{\link[=as.mo]{as.mo()}} to use the data for intelligent determination of microorganisms.
 }
diff --git a/man/catalogue_of_life_version.Rd b/man/catalogue_of_life_version.Rd
index a24d58064..55fbdc758 100644
--- a/man/catalogue_of_life_version.Rd
+++ b/man/catalogue_of_life_version.Rd
@@ -7,20 +7,20 @@
 catalogue_of_life_version()
 }
 \value{
-a \code{list}, which prints in pretty format
+a \code{\link{list}}, which prints in pretty format
 }
 \description{
 This function returns information about the included data from the Catalogue of Life.
 }
 \details{
-For DSMZ, see \code{?microorganisms}.
+For DSMZ, see \link{microorganisms}.
 }
 \section{Catalogue of Life}{
 
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Read more on our website!}{
@@ -34,5 +34,5 @@ microorganisms \%>\% freq(kingdom)
 microorganisms \%>\% group_by(kingdom) \%>\% freq(phylum, nmax = NULL)
 }
 \seealso{
-\code{\link{microorganisms}}
+\link{microorganisms}
 }
diff --git a/man/count.Rd b/man/count.Rd
index d5f82fa71..1ad0d81a1 100644
--- a/man/count.Rd
+++ b/man/count.Rd
@@ -12,7 +12,7 @@
 \alias{count_all}
 \alias{n_rsi}
 \alias{count_df}
-\title{Count isolates}
+\title{Count available isolates}
 \usage{
 count_resistant(..., only_all_tested = FALSE)
 
@@ -32,62 +32,63 @@ count_all(..., only_all_tested = FALSE)
 
 n_rsi(..., only_all_tested = FALSE)
 
-count_df(data, translate_ab = "name", language = get_locale(),
-  combine_SI = TRUE, combine_IR = FALSE)
+count_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
 }
 \arguments{
-\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.}
+\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link[=as.rsi]{as.rsi()}} if needed.}
 
-\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below}
+\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below}
 
-\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
+\item{data}{a \code{\link{data.frame}} containing columns with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}
 
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
+\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
 \item{combine_SI}{a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.}
 
 \item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}.}
 }
 \value{
-Integer
+An \code{\link{integer}}
 }
 \description{
-These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{\link[=summarise]{summarise()}} and support grouped variables, see \emph{Examples}.
 
-\code{count_resistant()} should be used to count resistant isolates, \code{count_susceptible()} should be used to count susceptible isolates.\cr
+\code{\link[=count_resistant]{count_resistant()}} should be used to count resistant isolates, \code{\link[=count_susceptible]{count_susceptible()}} should be used to count susceptible isolates.
 }
 \details{
-These functions are meant to count isolates. Use the \code{\link{resistance}}/\code{\link{susceptibility}} functions to calculate microbial resistance/susceptibility.
+These functions are meant to count isolates. Use the \code{\link[=resistance]{resistance()}}/\code{\link[=susceptibility]{susceptibility()}} functions to calculate microbial resistance/susceptibility.
 
-The function \code{count_resistant()} is equal to the function \code{count_R()}. The function \code{count_susceptible()} is equal to the function \code{count_SI()}.
+The function \code{\link[=count_resistant]{count_resistant()}} is equal to the function \code{\link[=count_R]{count_R()}}. The function \code{\link[=count_susceptible]{count_susceptible()}} is equal to the function \code{\link[=count_SI]{count_SI()}}.
 
-The function \code{n_rsi()} is an alias of \code{count_all()}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}()}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
+The function \code{\link[=n_rsi]{n_rsi()}} is an alias of \code{\link[=count_all]{count_all()}}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link[=n_distinct]{n_distinct()}}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
 
-The function \code{count_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and counts the number of S's, I's and R's. The function \code{rsi_df()} works exactly like \code{count_df()}, but adds the percentage of S, I and R.
+The function \code{\link[=count_df]{count_df()}} takes any variable from \code{data} that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) and counts the number of S's, I's and R's. The function \code{\link[=rsi_df]{rsi_df()}} works exactly like \code{\link[=count_df]{count_df()}}, but adds the percentage of S, I and R.
 }
 \section{Interpretation of S, I and R}{
 
 In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
 \itemize{
-  \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
-  \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
-  \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
+\item \strong{S} - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+\item \strong{I} - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
+\item \strong{R} - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
 }
 
 Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
 
-This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates.
+This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
 }
 
 \section{Combination therapy}{
 
-When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI:
-
-\preformatted{
---------------------------------------------------------------------
+When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:\preformatted{--------------------------------------------------------------------
                     only_all_tested = FALSE  only_all_tested = TRUE
                     -----------------------  -----------------------
  Drug A    Drug B   include as  include as   include as  include as
@@ -105,14 +106,11 @@ When using more than one variable for \code{...} (= combination therapy)), use \
 --------------------------------------------------------------------
 }
 
-Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:
-\preformatted{
-    count_S()    +   count_I()    +   count_R()    = count_all()
+Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:\preformatted{    count_S()    +   count_I()    +   count_R()    = count_all()
   proportion_S() + proportion_I() + proportion_R() = 1
 }
-and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:
-\preformatted{
-    count_S()    +   count_I()    +   count_R()    >= count_all()
+
+and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:\preformatted{    count_S()    +   count_I()    +   count_R()    >= count_all()
   proportion_S() + proportion_I() + proportion_R() >= 1
 }
 
@@ -184,5 +182,5 @@ example_isolates \%>\%
 
 }
 \seealso{
-\code{\link{proportion}_*} to calculate microbial resistance and susceptibility.
+\code{\link[=proportion]{proportion_*}} to calculate microbial resistance and susceptibility.
 }
diff --git a/man/eucast_rules.Rd b/man/eucast_rules.Rd
index 764eed66d..b9f5697e6 100644
--- a/man/eucast_rules.Rd
+++ b/man/eucast_rules.Rd
@@ -6,29 +6,29 @@
 \title{EUCAST rules}
 \source{
 \itemize{
-    \item{
-      EUCAST Expert Rules. Version 2.0, 2012. \cr
-      Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
-      \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
-    }
-    \item{
-      EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016.  \cr
-      \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
-    }
-    \item{
-      EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. \cr
-      \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx}
-    }
-  }
+\item EUCAST Expert Rules. Version 2.0, 2012. \cr
+Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
+\url{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
+\item EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes Tables. Version 3.1, 2016.  \cr
+\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
+\item EUCAST Breakpoint tables for interpretation of MICs and zone diameters. Version 9.0, 2019. \cr
+\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/v_9.0_Breakpoint_Tables.xlsx}
+}
 }
 \usage{
-eucast_rules(x, col_mo = NULL, info = TRUE, rules = c("breakpoints",
-  "expert", "other", "all"), verbose = FALSE, ...)
+eucast_rules(
+  x,
+  col_mo = NULL,
+  info = TRUE,
+  rules = c("breakpoints", "expert", "other", "all"),
+  verbose = FALSE,
+  ...
+)
 }
 \arguments{
 \item{x}{data with antibiotic columns, like e.g. \code{AMX} and \code{AMC}}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
 \item{info}{print progress}
 
@@ -36,120 +36,120 @@ eucast_rules(x, col_mo = NULL, info = TRUE, rules = c("breakpoints",
 
 \item{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.}
 
-\item{...}{column name of an antibiotic, see section Antibiotics}
+\item{...}{column name of an antibiotic, please see section \emph{Antibiotics} below}
 }
 \value{
-The input of \code{x}, possibly with edited values of antibiotics. Or, if \code{verbose = TRUE}, a \code{data.frame} with all original and new values of the affected bug-drug combinations.
+The input of \code{x}, possibly with edited values of antibiotics. Or, if \code{verbose = TRUE}, a \code{\link{data.frame}} with all original and new values of the affected bug-drug combinations.
 }
 \description{
-Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables. 
+Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables.
 
 To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules are applied at default, see Details.
 }
 \details{
-\strong{Note:} This function does not translate MIC values to RSI values. Use \code{\link{as.rsi}} for that. \cr
+\strong{Note:} This function does not translate MIC values to RSI values. Use \code{\link[=as.rsi]{as.rsi()}} for that. \cr
 \strong{Note:} When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.
 
 Before further processing, some non-EUCAST rules are applied to improve the efficacy of the EUCAST rules. These non-EUCAST rules, that are applied to all isolates, are:
 \itemize{
-  \item{Inherit amoxicillin (AMX) from ampicillin (AMP), where amoxicillin (AMX) is unavailable;}
-  \item{Inherit ampicillin (AMP) from amoxicillin (AMX), where ampicillin (AMP) is unavailable;}
-  \item{Set amoxicillin (AMX) = R where amoxicillin/clavulanic acid (AMC) = R;}
-  \item{Set piperacillin (PIP) = R where piperacillin/tazobactam (TZP) = R;}
-  \item{Set trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R;}
-  \item{Set amoxicillin/clavulanic acid (AMC) = S where amoxicillin (AMX) = S;}
-  \item{Set piperacillin/tazobactam (TZP) = S where piperacillin (PIP) = S;}
-  \item{Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S.}
-}
+\item Inherit amoxicillin (AMX) from ampicillin (AMP), where amoxicillin (AMX) is unavailable;
+\item Inherit ampicillin (AMP) from amoxicillin (AMX), where ampicillin (AMP) is unavailable;
+\item Set amoxicillin (AMX) = R where amoxicillin/clavulanic acid (AMC) = R;
+\item Set piperacillin (PIP) = R where piperacillin/tazobactam (TZP) = R;
+\item Set trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R;
+\item Set amoxicillin/clavulanic acid (AMC) = S where amoxicillin (AMX) = S;
+\item Set piperacillin/tazobactam (TZP) = S where piperacillin (PIP) = S;
+\item Set trimethoprim/sulfamethoxazole (SXT) = S where trimethoprim (TMP) = S.
 To \emph{not} use these rules, please use \code{eucast_rules(..., rules = c("breakpoints", "expert"))}.
+}
 
 The file containing all EUCAST rules is located here: \url{https://gitlab.com/msberends/AMR/blob/master/data-raw/eucast_rules.tsv}.
 }
 \section{Antibiotics}{
 
-To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
+To define antibiotics column names, leave as it is to determine it automatically with \code{\link[=guess_ab_col]{guess_ab_col()}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
 
-The following antibiotics are used for the functions \code{\link{eucast_rules}} and \code{\link{mdro}}. These are shown below in the format '\strong{antimicrobial ID}: name (\href{https://www.whocc.no/atc/structure_and_principles/}{ATC code})', sorted by name:
+The following antibiotics are used for the functions \code{\link[=eucast_rules]{eucast_rules()}} and \code{\link[=mdro]{mdro()}}. These are shown below in the format '\strong{antimicrobial ID}: name (\href{https://www.whocc.no/atc/structure_and_principles/}{ATC code})', sorted by name:
 
- \strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
- \strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
- \strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
- \strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
- \strong{SAM}: ampicillin/sulbactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR01}{J01CR01}),
- \strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
- \strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
- \strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
- \strong{CAP}: capreomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB30}{J04AB30}),
- \strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
- \strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
- \strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
- \strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
- \strong{CTT}: cefotetan (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC05}{J01DC05}),
- \strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
- \strong{CPT}: ceftaroline (\href{https://www.whocc.no/atc_ddd_index/?code=J01DI02}{J01DI02}),
- \strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
- \strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
- \strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
- \strong{CED}: cephradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
- \strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
- \strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
- \strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
- \strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
- \strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
- \strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
- \strong{DOR}: doripenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH04}{J01DH04}),
- \strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
- \strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
- \strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
- \strong{ETH}: ethambutol (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK02}{J04AK02}),
- \strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
- \strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
- \strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
- \strong{GAT}: gatifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA16}{J01MA16}),
- \strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
- \strong{GEH}: gentamicin-high (no ATC code),
- \strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
- \strong{INH}: isoniazid (\href{https://www.whocc.no/atc_ddd_index/?code=J04AC01}{J04AC01}),
- \strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
- \strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
- \strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
- \strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
- \strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
- \strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01XD01}{J01XD01}),
- \strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
- \strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
- \strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
- \strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
- \strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
- \strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
- \strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
- \strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
- \strong{NOV}: novobiocin (\href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
- \strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
- \strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF04}{J01CF04}),
- \strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
- \strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
- \strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
- \strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
- \strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
- \strong{PZA}: pyrazinamide (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK01}{J04AK01}),
- \strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
- \strong{RIB}: rifabutin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB04}{J04AB04}),
- \strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
- \strong{RFP}: rifapentine (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB05}{J04AB05}),
- \strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
- \strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
- \strong{STH}: streptomycin-high (no ATC code),
- \strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
- \strong{TLV}: telavancin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA03}{J01XA03}),
- \strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
- \strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
- \strong{TCC}: ticarcillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR03}{J01CR03}),
- \strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
- \strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
- \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
- \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
- \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
+\strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
+\strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
+\strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
+\strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
+\strong{SAM}: ampicillin/sulbactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR01}{J01CR01}),
+\strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
+\strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
+\strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
+\strong{CAP}: capreomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB30}{J04AB30}),
+\strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
+\strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
+\strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
+\strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
+\strong{CTT}: cefotetan (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC05}{J01DC05}),
+\strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
+\strong{CPT}: ceftaroline (\href{https://www.whocc.no/atc_ddd_index/?code=J01DI02}{J01DI02}),
+\strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
+\strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
+\strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
+\strong{CED}: cephradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
+\strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
+\strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
+\strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
+\strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
+\strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
+\strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
+\strong{DOR}: doripenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH04}{J01DH04}),
+\strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
+\strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
+\strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
+\strong{ETH}: ethambutol (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK02}{J04AK02}),
+\strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
+\strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
+\strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
+\strong{GAT}: gatifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA16}{J01MA16}),
+\strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
+\strong{GEH}: gentamicin-high (no ATC code),
+\strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
+\strong{INH}: isoniazid (\href{https://www.whocc.no/atc_ddd_index/?code=J04AC01}{J04AC01}),
+\strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
+\strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
+\strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
+\strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
+\strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
+\strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01XD01}{J01XD01}),
+\strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
+\strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
+\strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
+\strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
+\strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
+\strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
+\strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
+\strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
+\strong{NOV}: novobiocin (\href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
+\strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
+\strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF04}{J01CF04}),
+\strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
+\strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
+\strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
+\strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
+\strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
+\strong{PZA}: pyrazinamide (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK01}{J04AK01}),
+\strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
+\strong{RIB}: rifabutin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB04}{J04AB04}),
+\strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
+\strong{RFP}: rifapentine (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB05}{J04AB05}),
+\strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
+\strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
+\strong{STH}: streptomycin-high (no ATC code),
+\strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
+\strong{TLV}: telavancin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA03}{J01XA03}),
+\strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
+\strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
+\strong{TCC}: ticarcillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR03}{J01CR03}),
+\strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
+\strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
+\strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
+\strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
+\strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
 }
 
 \section{Read more on our website!}{
diff --git a/man/example_isolates.Rd b/man/example_isolates.Rd
index e94b71826..4eed7482d 100644
--- a/man/example_isolates.Rd
+++ b/man/example_isolates.Rd
@@ -5,17 +5,17 @@
 \alias{example_isolates}
 \title{Data set with 2,000 example isolates}
 \format{A \code{\link{data.frame}} with 2,000 observations and 49 variables:
-\describe{
-  \item{\code{date}}{date of receipt at the laboratory}
-  \item{\code{hospital_id}}{ID of the hospital, from A to D}
-  \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
-  \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
-  \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
-  \item{\code{age}}{age of the patient}
-  \item{\code{gender}}{gender of the patient}
-  \item{\code{patient_id}}{ID of the patient}
-  \item{\code{mo}}{ID of microorganism created with \code{\link{as.mo}}, see also \code{\link{microorganisms}}}
-  \item{\code{PEN:RIF}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{ab_name}}}
+\itemize{
+\item \code{date}\cr date of receipt at the laboratory
+\item \code{hospital_id}\cr ID of the hospital, from A to D
+\item \code{ward_icu}\cr logical to determine if ward is an intensive care unit
+\item \code{ward_clinical}\cr logical to determine if ward is a regular clinical ward
+\item \code{ward_outpatient}\cr logical to determine if ward is an outpatient clinic
+\item \code{age}\cr age of the patient
+\item \code{gender}\cr gender of the patient
+\item \code{patient_id}\cr ID of the patient
+\item \code{mo}\cr ID of microorganism created with \code{\link[=as.mo]{as.mo()}}, see also \link{microorganisms}
+\item \code{PEN:RIF}\cr 40 different antibiotics with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}}); these column names occur in \link{antibiotics} data set and can be translated with \code{\link[=ab_name]{ab_name()}}
 }}
 \usage{
 example_isolates
diff --git a/man/filter_ab_class.Rd b/man/filter_ab_class.Rd
index 9bb77f0d4..7c87ade75 100644
--- a/man/filter_ab_class.Rd
+++ b/man/filter_ab_class.Rd
@@ -45,7 +45,7 @@ filter_tetracyclines(x, result = NULL, scope = "any", ...)
 \arguments{
 \item{x}{a data set}
 
-\item{ab_class}{an antimicrobial class, like \code{"carbapenems"}, as can be found in \code{AMR::antibiotics$group}}
+\item{ab_class}{an antimicrobial class, like \code{"carbapenems"}, as can be found in \code{\link[=antibiotics]{AMR::antibiotics$group}}}
 
 \item{result}{an antibiotic result: S, I or R (or a combination of more of them)}
 
@@ -57,7 +57,7 @@ filter_tetracyclines(x, result = NULL, scope = "any", ...)
 Filter isolates on results in specific antibiotic variables based on their class (ATC groups). This makes it easy to get a list of isolates that were tested for e.g. any aminoglycoside.
 }
 \details{
-The \code{group} column in \code{\link{antibiotics}} data set will be searched for \code{ab_class} (case-insensitive). If no results are found, the \code{atc_group1} and \code{atc_group2} columns will be searched. Next, \code{x} will be checked for column names with a value in any abbreviations, codes or official names found in the \code{antibiotics} data set.
+The \code{group} column in \link{antibiotics} data set will be searched for \code{ab_class} (case-insensitive). If no results are found, the \code{atc_group1} and \code{atc_group2} columns will be searched. Next, \code{x} will be checked for column names with a value in any abbreviations, codes or official names found in the \link{antibiotics} data set.
 }
 \examples{
 library(dplyr)
diff --git a/man/first_isolate.Rd b/man/first_isolate.Rd
index 6ef7bf836..013e1cc53 100755
--- a/man/first_isolate.Rd
+++ b/man/first_isolate.Rd
@@ -6,31 +6,57 @@
 \alias{filter_first_weighted_isolate}
 \title{Determine first (weighted) isolates}
 \source{
-Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
+Methodology of this function is based on:
+
+\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
 }
 \usage{
-first_isolate(x, col_date = NULL, col_patient_id = NULL,
-  col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
-  col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
-  testcodes_exclude = NULL, icu_exclude = FALSE,
-  specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
-  points_threshold = 2, info = TRUE, include_unknown = FALSE, ...)
+first_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  col_testcode = NULL,
+  col_specimen = NULL,
+  col_icu = NULL,
+  col_keyantibiotics = NULL,
+  episode_days = 365,
+  testcodes_exclude = NULL,
+  icu_exclude = FALSE,
+  specimen_group = NULL,
+  type = "keyantibiotics",
+  ignore_I = TRUE,
+  points_threshold = 2,
+  info = TRUE,
+  include_unknown = FALSE,
+  ...
+)
 
-filter_first_isolate(x, col_date = NULL, col_patient_id = NULL,
-  col_mo = NULL, ...)
+filter_first_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  ...
+)
 
-filter_first_weighted_isolate(x, col_date = NULL,
-  col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
-  ...)
+filter_first_weighted_isolate(
+  x,
+  col_date = NULL,
+  col_patient_id = NULL,
+  col_mo = NULL,
+  col_keyantibiotics = NULL,
+  ...
+)
 }
 \arguments{
-\item{x}{a \code{data.frame} containing isolates.}
+\item{x}{a \code{\link{data.frame}} containing isolates.}
 
 \item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
 
 \item{col_patient_id}{column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
 \item{col_testcode}{column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.}
 
@@ -38,7 +64,7 @@ filter_first_weighted_isolate(x, col_date = NULL,
 
 \item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
 
-\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this.}
+\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link[=key_antibiotics]{key_antibiotics()}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this.}
 
 \item{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.}
 
@@ -58,30 +84,27 @@ filter_first_weighted_isolate(x, col_date = NULL,
 
 \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{...}{parameters passed on to the \code{first_isolate} function}
+\item{...}{parameters passed on to the \code{\link[=first_isolate]{first_isolate()}} function}
 }
 \value{
-Logical vector
+A \code{\link{logical}} vector
 }
 \description{
 Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
 }
 \details{
 \strong{WHY THIS IS SO IMPORTANT} \cr
-To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
+To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [\link{1}](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
 
 All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
 
-The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
-\preformatted{
- x \%>\%
+The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to:\preformatted{ x \%>\%
    mutate(only_firsts = first_isolate(x, ...)) \%>\%
    filter(only_firsts == TRUE) \%>\%
    select(-only_firsts)
 }
-The function \code{filter_first_weighted_isolate} is essentially equal to:
-\preformatted{
- x \%>\%
+
+The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} is essentially equal to:\preformatted{ x \%>\%
    mutate(keyab = key_antibiotics(.)) \%>\%
    mutate(only_weighted_firsts = first_isolate(x,
                                                col_keyantibiotics = "keyab", ...)) \%>\%
@@ -91,13 +114,15 @@ The function \code{filter_first_weighted_isolate} is essentially equal to:
 }
 \section{Key antibiotics}{
 
-    There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
+There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results:
+\enumerate{
+\item Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}
 
-    \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
-    Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr
+Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link[=key_antibiotics]{key_antibiotics()}} function.
+\item Using \code{type = "points"} and parameter \code{points_threshold}
 
-    \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
-    A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
+A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
+}
 }
 
 \section{Read more on our website!}{
@@ -158,5 +183,5 @@ x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
 }
 }
 \seealso{
-\code{\link{key_antibiotics}}
+\code{\link[=key_antibiotics]{key_antibiotics()}}
 }
diff --git a/man/g.test.Rd b/man/g.test.Rd
index a0f633dd0..fee57e8d6 100644
--- a/man/g.test.Rd
+++ b/man/g.test.Rd
@@ -4,16 +4,15 @@
 \alias{g.test}
 \title{\emph{G}-test for Count Data}
 \source{
-This code is almost identical to \code{\link{chisq.test}}, except that:
+The code for this function is identical to that of \code{\link[=chisq.test]{chisq.test()}}, except that:
 \itemize{
-  \item{The calculation of the statistic was changed to \code{2 * sum(x * log(x / E))}}
-  \item{Yates' continuity correction was removed as it does not apply to a \emph{G}-test}
-  \item{The possibility to simulate p values with \code{simulate.p.value} was removed}
+\item The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))}
+\item Yates' continuity correction was removed as it does not apply to a \emph{G}-test
+\item The possibility to simulate p values with \code{simulate.p.value} was removed
 }
 }
 \usage{
-g.test(x, y = NULL, p = rep(1/length(x), length(x)),
-  rescale.p = FALSE)
+g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)
 }
 \arguments{
 \item{x}{a numeric vector or matrix. \code{x} and \code{y} can also
@@ -50,56 +49,54 @@ A list with class \code{"htest"} containing the following
     section 2.4.5 for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
 }
 \description{
-\code{g.test} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link{chisq.test}} but is more reliable [1]. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
+\code{\link[=g.test]{g.test()}} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link[=chisq.test]{chisq.test()}} but is more reliable \link{1}. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
 }
 \details{
 If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given.
 
-  If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers.  Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these.  Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
+If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers.  Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these.  Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
 
-  The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
+The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
 
-  In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test.
+In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test.
 
-  In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow.
-}
-\section{\emph{G}-test of goodness-of-fit (likelihood ratio test)}{
+In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow.
+\subsection{\emph{G}-test of goodness-of-fit (likelihood ratio test)}{
 
 Use the \emph{G}-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).
 
-If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link{fisher.test}}).
+If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link[=fisher.test]{fisher.test()}}).
 
-The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link{chisq.test}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
+The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link[=chisq.test]{chisq.test()}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
 }
 
-\section{\emph{G}-test of independence}{
+\subsection{\emph{G}-test of independence}{
 
 Use the \emph{G}-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.
 
 It is also possible to do a \emph{G}-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.
 
-Fisher's exact test (\code{\link{fisher.test}}) is an \strong{exact} test, where the \emph{G}-test is still only an \strong{approximation}. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.
+Fisher's exact test (\code{\link[=fisher.test]{fisher.test()}}) is an \strong{exact} test, where the \emph{G}-test is still only an \strong{approximation}. For any 2x2 table, Fisher's Exact test may be slower but will still run in seconds, even if the sum of your observations is multiple millions.
 
-The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link{chisq.test}}), and they will give approximately the same results.
+The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link[=chisq.test]{chisq.test()}}), and they will give approximately the same results.
 }
 
-\section{How the test works}{
+\subsection{How the test works}{
 
-Unlike the exact test of goodness-of-fit (\code{\link{fisher.test}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
+Unlike the exact test of goodness-of-fit (\code{\link[=fisher.test]{fisher.test()}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
 
 The \emph{G}-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a \emph{G}-statistic is:
 
-\code{G <- 2 * sum(x * log(x / E))}
+\eqn{G = 2 * sum(x * log(x / E))}
 
-where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated with:
-
-\code{p <- stats::pchisq(G, df, lower.tail = FALSE)}
+where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated in \R with:\preformatted{p <- stats::pchisq(G, df, lower.tail = FALSE)
+}
 
 where \code{df} are the degrees of freedom.
 
 If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course.
 }
-
+}
 \section{Read more on our website!}{
 
 On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
@@ -140,8 +137,8 @@ g.test(x)
 
 }
 \references{
-[1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
+\link{1} McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
 }
 \seealso{
-\code{\link{chisq.test}}
+\code{\link[=chisq.test]{chisq.test()}}
 }
diff --git a/man/ggplot_rsi.Rd b/man/ggplot_rsi.Rd
index 09e6f6877..7b60af056 100644
--- a/man/ggplot_rsi.Rd
+++ b/man/ggplot_rsi.Rd
@@ -10,36 +10,66 @@
 \alias{labels_rsi_count}
 \title{AMR plots with \code{ggplot2}}
 \usage{
-ggplot_rsi(data, position = NULL, x = "antibiotic",
-  fill = "interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
-  limits = NULL, translate_ab = "name", combine_SI = TRUE,
-  combine_IR = FALSE, language = get_locale(), nrow = NULL,
-  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR =
-  "#ff6961", R = "#ff6961"), datalabels = TRUE, datalabels.size = 2.5,
-  datalabels.colour = "gray15", title = NULL, subtitle = NULL,
-  caption = NULL, x.title = "Antimicrobial", y.title = "Proportion",
-  ...)
+ggplot_rsi(
+  data,
+  position = NULL,
+  x = "antibiotic",
+  fill = "interpretation",
+  facet = NULL,
+  breaks = seq(0, 1, 0.1),
+  limits = NULL,
+  translate_ab = "name",
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  language = get_locale(),
+  nrow = NULL,
+  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
+    "#ff6961"),
+  datalabels = TRUE,
+  datalabels.size = 2.5,
+  datalabels.colour = "gray15",
+  title = NULL,
+  subtitle = NULL,
+  caption = NULL,
+  x.title = "Antimicrobial",
+  y.title = "Proportion",
+  ...
+)
 
-geom_rsi(position = NULL, x = c("antibiotic", "interpretation"),
-  fill = "interpretation", translate_ab = "name",
-  language = get_locale(), combine_SI = TRUE, combine_IR = FALSE,
-  ...)
+geom_rsi(
+  position = NULL,
+  x = c("antibiotic", "interpretation"),
+  fill = "interpretation",
+  translate_ab = "name",
+  language = get_locale(),
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  ...
+)
 
 facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL)
 
 scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
 
-scale_rsi_colours(colours = c(S = "#61a8ff", SI = "#61a8ff", I =
-  "#61f7ff", IR = "#ff6961", R = "#ff6961"))
+scale_rsi_colours(
+  colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR = "#ff6961", R =
+    "#ff6961")
+)
 
 theme_rsi()
 
-labels_rsi_count(position = NULL, x = "antibiotic",
-  translate_ab = "name", combine_SI = TRUE, combine_IR = FALSE,
-  datalabels.size = 3, datalabels.colour = "gray15")
+labels_rsi_count(
+  position = NULL,
+  x = "antibiotic",
+  translate_ab = "name",
+  combine_SI = TRUE,
+  combine_IR = FALSE,
+  datalabels.size = 3,
+  datalabels.colour = "gray15"
+)
 }
 \arguments{
-\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
+\item{data}{a \code{\link{data.frame}} with column(s) of class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}
 
 \item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}}
 
@@ -53,19 +83,19 @@ labels_rsi_count(position = NULL, x = "antibiotic",
 
 \item{limits}{numeric vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
 
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
+\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
 
 \item{combine_SI}{a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.}
 
 \item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}.}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
 \item{nrow}{(when using \code{facet}) number of rows}
 
-\item{colours}{a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} to use default \code{ggplot2} colours.}
+\item{colours}{a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} to use default [ggplot2][\code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} colours.}
 
-\item{datalabels}{show datalabels using \code{labels_rsi_count}}
+\item{datalabels}{show datalabels using \code{\link[=labels_rsi_count]{labels_rsi_count()}}}
 
 \item{datalabels.size}{size of the datalabels}
 
@@ -81,28 +111,29 @@ labels_rsi_count(position = NULL, x = "antibiotic",
 
 \item{y.title}{text to show as y axis description}
 
-\item{...}{other parameters passed on to \code{geom_rsi}}
+\item{...}{other parameters passed on to \code{\link[=geom_rsi]{geom_rsi()}}}
 }
 \description{
-Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}2} functions.
+Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \link[ggplot2:ggplot]{ggplot2} functions.
 }
 \details{
-At default, the names of antibiotics will be shown on the plots using \code{\link{ab_name}}. This can be set with the \code{translate_ab} parameter. See \code{\link{count_df}}.
+At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} parameter. See \code{\link[=count_df]{count_df()}}.
+\subsection{The functions}{
 
-\strong{The functions}\cr
-\code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{rsi_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
+\code{\link[=geom_rsi]{geom_rsi()}} will take any variable from the data that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) using \code{\link[=rsi_df]{rsi_df()}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 
-\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
+\code{\link[=facet_rsi]{facet_rsi()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
 
-\code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.
+\code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_continuous()}}.
 
-\code{scale_rsi_colours} sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using \code{\link[ggplot2]{scale_brewer}}.
+\code{\link[=scale_rsi_colours]{scale_rsi_colours()}} sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using \code{\link[ggplot2:scale_brewer]{ggplot2::scale_brewer()}}.
 
-\code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.
+\code{\link[=theme_rsi]{theme_rsi()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
 
-\code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}}
+\code{\link[=labels_rsi_count]{labels_rsi_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}
 
-\code{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 (\code{\%>\%}). See Examples.
+\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 Examples.
+}
 }
 \section{Read more on our website!}{
 
diff --git a/man/guess_ab_col.Rd b/man/guess_ab_col.Rd
index 8f69b7aad..c5956a4db 100644
--- a/man/guess_ab_col.Rd
+++ b/man/guess_ab_col.Rd
@@ -7,9 +7,9 @@
 guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE)
 }
 \arguments{
-\item{x}{a \code{data.frame}}
+\item{x}{a \code{\link{data.frame}}}
 
-\item{search_string}{a text to search \code{x} for, will be checked with \code{\link{as.ab}} if this value is not a column in \code{x}}
+\item{search_string}{a text to search \code{x} for, will be checked with \code{\link[=as.ab]{as.ab()}} if this value is not a column in \code{x}}
 
 \item{verbose}{a logical to indicate whether additional info should be printed}
 }
@@ -17,10 +17,10 @@ guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE)
 A column name of \code{x}, or \code{NULL} when no result is found.
 }
 \description{
-This tries to find a column name in a data set based on information from the \code{\link{antibiotics}} data set. Also supports WHONET abbreviations.
+This tries to find a column name in a data set based on information from the \link{antibiotics} data set. Also supports WHONET abbreviations.
 }
 \details{
-You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \code{\link{antibiotics}} data set for any column containing a name or code of that antibiotic. \strong{Longer columns names take precendence over shorter column names.}
+You can look for an antibiotic (trade) name or abbreviation and it will search \code{x} and the \link{antibiotics} data set for any column containing a name or code of that antibiotic. \strong{Longer columns names take precendence over shorter column names.}
 }
 \section{Read more on our website!}{
 
diff --git a/man/join.Rd b/man/join.Rd
index 454e1e56d..53aaac645 100755
--- a/man/join.Rd
+++ b/man/join.Rd
@@ -9,7 +9,7 @@
 \alias{full_join_microorganisms}
 \alias{semi_join_microorganisms}
 \alias{anti_join_microorganisms}
-\title{Join a table with \code{microorganisms}}
+\title{Join a table with \link{microorganisms}}
 \usage{
 inner_join_microorganisms(x, by = NULL, suffix = c("2", ""), ...)
 
@@ -26,17 +26,17 @@ anti_join_microorganisms(x, by = NULL, ...)
 \arguments{
 \item{x}{existing table to join, or character vector}
 
-\item{by}{a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})}
+\item{by}{a variable to join by - if left empty will search for a column with class \code{\link{mo}} (created with \code{\link[=as.mo]{as.mo()}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \link{microorganisms} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})}
 
 \item{suffix}{if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.}
 
-\item{...}{other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.}
+\item{...}{other parameters to pass on to \code{\link[dplyr:join]{dplyr::join()}}}
 }
 \description{
-Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
+Join the data set \link{microorganisms} easily to an existing table or character vector.
 }
 \details{
-\strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
+\strong{Note:} As opposed to the \code{\link[dplyr:join]{dplyr::join()}} functions of \code{dplyr}, \code{\link{characters}} vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr:join]{dplyr::join()}} for more information.
 }
 \section{Read more on our website!}{
 
diff --git a/man/key_antibiotics.Rd b/man/key_antibiotics.Rd
index ef4f9a974..49b499203 100755
--- a/man/key_antibiotics.Rd
+++ b/man/key_antibiotics.Rd
@@ -5,36 +5,50 @@
 \alias{key_antibiotics_equal}
 \title{Key antibiotics for first \emph{weighted} isolates}
 \usage{
-key_antibiotics(x, col_mo = NULL, universal_1 = guess_ab_col(x,
-  "amoxicillin"), universal_2 = guess_ab_col(x,
-  "amoxicillin/clavulanic acid"), universal_3 = guess_ab_col(x,
-  "cefuroxime"), universal_4 = guess_ab_col(x,
-  "piperacillin/tazobactam"), universal_5 = guess_ab_col(x,
-  "ciprofloxacin"), universal_6 = guess_ab_col(x,
-  "trimethoprim/sulfamethoxazole"), GramPos_1 = guess_ab_col(x,
-  "vancomycin"), GramPos_2 = guess_ab_col(x, "teicoplanin"),
+key_antibiotics(
+  x,
+  col_mo = NULL,
+  universal_1 = guess_ab_col(x, "amoxicillin"),
+  universal_2 = guess_ab_col(x, "amoxicillin/clavulanic acid"),
+  universal_3 = guess_ab_col(x, "cefuroxime"),
+  universal_4 = guess_ab_col(x, "piperacillin/tazobactam"),
+  universal_5 = guess_ab_col(x, "ciprofloxacin"),
+  universal_6 = guess_ab_col(x, "trimethoprim/sulfamethoxazole"),
+  GramPos_1 = guess_ab_col(x, "vancomycin"),
+  GramPos_2 = guess_ab_col(x, "teicoplanin"),
   GramPos_3 = guess_ab_col(x, "tetracycline"),
   GramPos_4 = guess_ab_col(x, "erythromycin"),
-  GramPos_5 = guess_ab_col(x, "oxacillin"), GramPos_6 = guess_ab_col(x,
-  "rifampin"), GramNeg_1 = guess_ab_col(x, "gentamicin"),
+  GramPos_5 = guess_ab_col(x, "oxacillin"),
+  GramPos_6 = guess_ab_col(x, "rifampin"),
+  GramNeg_1 = guess_ab_col(x, "gentamicin"),
   GramNeg_2 = guess_ab_col(x, "tobramycin"),
-  GramNeg_3 = guess_ab_col(x, "colistin"), GramNeg_4 = guess_ab_col(x,
-  "cefotaxime"), GramNeg_5 = guess_ab_col(x, "ceftazidime"),
-  GramNeg_6 = guess_ab_col(x, "meropenem"), warnings = TRUE, ...)
+  GramNeg_3 = guess_ab_col(x, "colistin"),
+  GramNeg_4 = guess_ab_col(x, "cefotaxime"),
+  GramNeg_5 = guess_ab_col(x, "ceftazidime"),
+  GramNeg_6 = guess_ab_col(x, "meropenem"),
+  warnings = TRUE,
+  ...
+)
 
-key_antibiotics_equal(y, z, type = c("keyantibiotics", "points"),
-  ignore_I = TRUE, points_threshold = 2, info = FALSE)
+key_antibiotics_equal(
+  y,
+  z,
+  type = c("keyantibiotics", "points"),
+  ignore_I = TRUE,
+  points_threshold = 2,
+  info = FALSE
+)
 }
 \arguments{
 \item{x}{table with antibiotics coloms, like \code{AMX} or \code{amox}}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
-\item{universal_1, universal_2, universal_3, universal_4, universal_5, universal_6}{column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.}
+\item{universal_1, universal_2, universal_3, universal_4, universal_5, universal_6}{column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link[=guess_ab_col]{guess_ab_col()}}.}
 
-\item{GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6}{column names of antibiotics for \strong{Gram-positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.}
+\item{GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6}{column names of antibiotics for \strong{Gram-positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link[=guess_ab_col]{guess_ab_col()}}.}
 
-\item{GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6}{column names of antibiotics for \strong{Gram-negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.}
+\item{GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6}{column names of antibiotics for \strong{Gram-negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link[=guess_ab_col]{guess_ab_col()}}.}
 
 \item{warnings}{give warning about missing antibiotic columns, they will anyway be ignored}
 
@@ -51,29 +65,56 @@ key_antibiotics_equal(y, z, type = c("keyantibiotics", "points"),
 \item{info}{print progress}
 }
 \description{
-These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
+These function can be used to determine first isolates (see \code{\link[=first_isolate]{first_isolate()}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
 }
 \details{
-The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not.
+The function \code{\link[=key_antibiotics]{key_antibiotics()}} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{\link[=key_antibiotics_equal]{key_antibiotics_equal()}}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link[=first_isolate]{first_isolate()}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link[=first_isolate]{first_isolate()}}). Without key antibiotic comparison it would not.
 
-  At default, the antibiotics that are used for \strong{Gram-positive bacteria} are: \cr
-  amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampin.
+At default, the antibiotics that are used for \strong{Gram-positive bacteria} are:
+\itemize{
+\item Amoxicillin
+\item Amoxicillin/clavulanic acid
+\item Cefuroxime
+\item Piperacillin/tazobactam
+\item Ciprofloxacin
+\item Trimethoprim/sulfamethoxazole
+\item Vancomycin
+\item Teicoplanin
+\item Tetracycline
+\item Erythromycin
+\item Oxacillin
+\item Rifampin
+}
 
-  At default, the antibiotics that are used for \strong{Gram-negative bacteria} are: \cr
-  amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem.
+At default the antibiotics that are used for \strong{Gram-negative bacteria} are:
+\itemize{
+\item Amoxicillin
+\item Amoxicillin/clavulanic acid
+\item Cefuroxime
+\item Piperacillin/tazobactam
+\item Ciprofloxacin
+\item Trimethoprim/sulfamethoxazole
+\item Gentamicin
+\item Tobramycin
+\item Colistin
+\item Cefotaxime
+\item Ceftazidime
+\item Meropenem
+}
 
-
-  The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
+The function \code{\link[=key_antibiotics_equal]{key_antibiotics_equal()}} checks the characters returned by \code{\link[=key_antibiotics]{key_antibiotics()}} for equality, and returns a \code{\link{logical}} vector.
 }
 \section{Key antibiotics}{
 
-    There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
+There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results:
+\enumerate{
+\item Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}
 
-    \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
-    Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr
+Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link[=key_antibiotics]{key_antibiotics()}} function.
+\item Using \code{type = "points"} and parameter \code{points_threshold}
 
-    \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
-    A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
+A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
+}
 }
 
 \section{Read more on our website!}{
@@ -112,5 +153,5 @@ key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
 # FALSE, because I is not ignored and so the 4th value differs
 }
 \seealso{
-\code{\link{first_isolate}}
+\code{\link[=first_isolate]{first_isolate()}}
 }
diff --git a/man/kurtosis.Rd b/man/kurtosis.Rd
index 09bff4373..59b08786a 100644
--- a/man/kurtosis.Rd
+++ b/man/kurtosis.Rd
@@ -16,7 +16,7 @@ kurtosis(x, na.rm = FALSE)
 \method{kurtosis}{data.frame}(x, na.rm = FALSE)
 }
 \arguments{
-\item{x}{a vector of values, a \code{matrix} or a \code{data frame}}
+\item{x}{a vector of values, a \code{\link{matrix}} or a \code{\link{data frame}}}
 
 \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.}
 }
@@ -29,5 +29,5 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{skewness}}
+\code{\link[=skewness]{skewness()}}
 }
diff --git a/man/like.Rd b/man/like.Rd
index 7ed6f949e..b4ea7b076 100755
--- a/man/like.Rd
+++ b/man/like.Rd
@@ -18,12 +18,12 @@ x \%like_case\% pattern
 \arguments{
 \item{x}{a character vector where matches are sought, or an
     object which can be coerced by \code{as.character} to a character
-    vector.  \link{Long vectors} are supported.}
+    vector.  \link[base]{Long vectors} are supported.}
 
-\item{pattern}{character string containing a \link{regular expression}
+\item{pattern}{character string containing a \link[base]{regular expression}
     (or character string for \code{fixed = TRUE}) to be matched
     in the given character vector.  Coerced by
-    \code{\link{as.character}} to a character string if possible.  If a
+    \code{\link[base]{as.character}} to a character string if possible.  If a
     character vector of length 2 or more is supplied, the first element
     is used with a warning.  Missing values are allowed except for
     \code{regexpr} and \code{gregexpr}.}
@@ -32,13 +32,13 @@ x \%like_case\% pattern
       sensitive} and if \code{TRUE}, case is ignored during matching.}
 }
 \value{
-A \code{logical} vector
+A \code{\link{logical}} vector
 }
 \description{
-Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive (use \code{a \%like_case\% b} for case-sensitive matching). Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors, or can both have the same length to iterate over all cases.
+Convenient wrapper around \code{\link[base:grep]{base::grep()}} to match a pattern: \code{a \%like\% b}. It always returns a \code{\link{logical}} vector and is always case-insensitive (use \code{a \%like_case\% b} for case-sensitive matching). Also, \code{pattern} (\emph{b}) can be as long as \code{x} (\emph{a}) to compare items of each index in both vectors, or can both have the same length to iterate over all cases.
 }
 \details{
-Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
+Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like \code{Ctrl+Shift+L} or \code{Cmd+Shift+L} (see \code{Tools} > \verb{Modify Keyboard Shortcuts...}).
 }
 \section{Read more on our website!}{
 
@@ -67,5 +67,5 @@ example_isolates \%>\%
   freq(mo_fullname(mo))
 }
 \seealso{
-\code{\link[base]{grep}}
+\code{\link[base:grep]{base::grep()}}
 }
diff --git a/man/mdro.Rd b/man/mdro.Rd
index 56558457f..0a824c014 100644
--- a/man/mdro.Rd
+++ b/man/mdro.Rd
@@ -15,12 +15,19 @@
 \alias{eucast_exceptional_phenotypes}
 \title{Determine multidrug-resistant organisms (MDRO)}
 \source{
-Please see Details for the list of publications used for this function.
+Please see \emph{Details} for the list of publications used for this function.
 }
 \usage{
-mdro(x, guideline = NULL, col_mo = NULL, info = TRUE,
-  pct_required_classes = 0.5, combine_SI = TRUE, verbose = FALSE,
-  ...)
+mdro(
+  x,
+  guideline = "CMI2012",
+  col_mo = NULL,
+  info = TRUE,
+  pct_required_classes = 0.5,
+  combine_SI = TRUE,
+  verbose = FALSE,
+  ...
+)
 
 brmo(x, guideline = "BRMO", ...)
 
@@ -35,26 +42,30 @@ eucast_exceptional_phenotypes(x, guideline = "EUCAST", ...)
 \arguments{
 \item{x}{data with antibiotic columns, like e.g. \code{AMX} and \code{AMC}}
 
-\item{guideline}{a specific guideline to follow. When left empty, the publication by Magiorakos \emph{et al.} (2012, Clinical Microbiology and Infection) will be followed, see Details.}
+\item{guideline}{a specific guideline to follow. When left empty, the publication by Magiorakos \emph{et al.} (2012, Clinical Microbiology and Infection) will be followed, please see \emph{Details}.}
 
-\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
+\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
 
-\item{info}{print progress}
+\item{info}{a logical to indicate whether progress should be printed to the console}
 
 \item{pct_required_classes}{minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for \emph{S. aureus}. Setting this \code{pct_required_classes} argument to \code{0.5} (default) means that for every \emph{S. aureus} isolate at least 8 different classes must be available. Any lower number of available classes will return \code{NA} for that isolate.}
 
-\item{combine_SI}{a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the \code{mdro()} function, it follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using \code{combine_SI = FALSE}, resistance is considered when isolates are R or I.}
+\item{combine_SI}{a logical to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the \code{\link[=mdro]{mdro()}} function, it follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using \code{combine_SI = FALSE}, resistance is considered when isolates are R or I.}
 
 \item{verbose}{a logical to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.}
 
-\item{...}{column name of an antibiotic, see section Antibiotics}
+\item{...}{column name of an antibiotic, please see section \emph{Antibiotics} below}
 }
 \value{
 \itemize{
-  \item{CMI 2012 paper - function \code{mdr_cmi2012()} or \code{mdro()}:\cr Ordered factor with levels \code{Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR)}}
-  \item{TB guideline - function \code{mdr_tb()} or \code{mdro(..., guideline = "TB")}:\cr Ordered factor with levels \code{Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant}}
-  \item{German guideline - function \code{mrgn()} or \code{mdro(..., guideline = "MRGN")}:\cr Ordered factor with levels \code{Negative < 3MRGN < 4MRGN}}
-  \item{Everything else:\cr Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}. The value \code{"Positive, unconfirmed"} means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests}
+\item CMI 2012 paper - function \code{\link[=mdr_cmi2012]{mdr_cmi2012()}} or \code{\link[=mdro]{mdro()}}:\cr
+Ordered \code{\link{factor}} with levels \code{Negative} < \code{Multi-drug-resistant (MDR)} < \verb{Extensively drug-resistant (XDR)} < \code{Pandrug-resistant (PDR)}
+\item TB guideline - function \code{\link[=mdr_tb]{mdr_tb()}} or \code{\link[=mdro]{mdro(..., guideline = "TB")}}:\cr
+Ordered \code{\link{factor}} with levels \code{Negative} < \code{Mono-resistant} < \code{Poly-resistant} < \code{Multi-drug-resistant} < \verb{Extensively drug-resistant}
+\item German guideline - function \code{\link[=mrgn]{mrgn()}} or \code{\link[=mdro]{mdro(..., guideline = "MRGN")}}:\cr
+Ordered \code{\link{factor}} with levels \code{Negative} < \verb{3MRGN} < \verb{4MRGN}
+\item Everything else:\cr
+Ordered \code{\link{factor}} with levels \code{Negative} < \verb{Positive, unconfirmed} < \code{Positive}. The value \code{"Positive, unconfirmed"} means that, according to the guideline, it is not entirely sure if the isolate is multi-drug resistant and this should be confirmed with additional (e.g. molecular) tests
 }
 }
 \description{
@@ -65,117 +76,121 @@ For the \code{pct_required_classes} argument, values above 1 will be divided by
 
 Currently supported guidelines are (case-insensitive):
 \itemize{
-  \item{\code{guideline = "CMI2012"}: Magiorakos AP, Srinivasan A \emph{et al.} "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (\href{https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext}{link})}
-  \item{\code{guideline = "EUCAST"}: The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\href{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{link})}
-  \item{\code{guideline = "TB"}: The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (\href{https://www.who.int/tb/publications/pmdt_companionhandbook/en/}{link})}
-  \item{\code{guideline = "MRGN"}: The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6}
-  \item{\code{guideline = "BRMO"}: The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" (\href{https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH}{link})}
+\item \code{guideline = "CMI2012"}\cr
+Magiorakos AP, Srinivasan A \emph{et al.} "Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance." Clinical Microbiology and Infection (2012) (\href{https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext}{link})
+\item \code{guideline = "EUCAST"}\cr
+The European international guideline - EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\href{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{link})
+\item \code{guideline = "TB"}\cr
+The international guideline for multi-drug resistant tuberculosis - World Health Organization "Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis" (\href{https://www.who.int/tb/publications/pmdt_companionhandbook/en/}{link})
+\item \code{guideline = "MRGN"}\cr
+The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
+\item \code{guideline = "BRMO"}\cr
+The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) \link{ZKH}" (\href{https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH}{link})
 }
 
 Please suggest your own (country-specific) guidelines by letting us know: \url{https://gitlab.com/msberends/AMR/issues/new}.
 
-\strong{Note:} Every test that involves the Enterobacteriaceae family, will internally be performed using its newly named order Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeolu \emph{et al.} in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by this \code{mdro()} function makes sure that results from before 2016 and after 2016 are identical.
+\strong{Note:} Every test that involves the Enterobacteriaceae family, will internally be performed using its newly named order Enterobacterales, since the Enterobacteriaceae family has been taxonomically reclassified by Adeolu \emph{et al.} in 2016. Before that, Enterobacteriaceae was the only family under the Enterobacteriales (with an i) order. All species under the old Enterobacteriaceae family are still under the new Enterobacterales (without an i) order, but divided into multiple families. The way tests are performed now by this \code{\link[=mdro]{mdro()}} function makes sure that results from before 2016 and after 2016 are identical.
 }
 \section{Antibiotics}{
 
-To define antibiotics column names, leave as it is to determine it automatically with \code{\link{guess_ab_col}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
+To define antibiotics column names, leave as it is to determine it automatically with \code{\link[=guess_ab_col]{guess_ab_col()}} or input a text (case-insensitive), or use \code{NULL} to skip a column (e.g. \code{TIC = NULL} to skip ticarcillin). Manually defined but non-existing columns will be skipped with a warning.
 
-The following antibiotics are used for the functions \code{\link{eucast_rules}} and \code{\link{mdro}}. These are shown below in the format '\strong{antimicrobial ID}: name (\href{https://www.whocc.no/atc/structure_and_principles/}{ATC code})', sorted by name:
+The following antibiotics are used for the functions \code{\link[=eucast_rules]{eucast_rules()}} and \code{\link[=mdro]{mdro()}}. These are shown below in the format '\strong{antimicrobial ID}: name (\href{https://www.whocc.no/atc/structure_and_principles/}{ATC code})', sorted by name:
 
- \strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
- \strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
- \strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
- \strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
- \strong{SAM}: ampicillin/sulbactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR01}{J01CR01}),
- \strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
- \strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
- \strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
- \strong{CAP}: capreomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB30}{J04AB30}),
- \strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
- \strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
- \strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
- \strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
- \strong{CTT}: cefotetan (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC05}{J01DC05}),
- \strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
- \strong{CPT}: ceftaroline (\href{https://www.whocc.no/atc_ddd_index/?code=J01DI02}{J01DI02}),
- \strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
- \strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
- \strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
- \strong{CED}: cephradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
- \strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
- \strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
- \strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
- \strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
- \strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
- \strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
- \strong{DOR}: doripenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH04}{J01DH04}),
- \strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
- \strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
- \strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
- \strong{ETH}: ethambutol (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK02}{J04AK02}),
- \strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
- \strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
- \strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
- \strong{GAT}: gatifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA16}{J01MA16}),
- \strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
- \strong{GEH}: gentamicin-high (no ATC code),
- \strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
- \strong{INH}: isoniazid (\href{https://www.whocc.no/atc_ddd_index/?code=J04AC01}{J04AC01}),
- \strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
- \strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
- \strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
- \strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
- \strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
- \strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01XD01}{J01XD01}),
- \strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
- \strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
- \strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
- \strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
- \strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
- \strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
- \strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
- \strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
- \strong{NOV}: novobiocin (\href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
- \strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
- \strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF04}{J01CF04}),
- \strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
- \strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
- \strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
- \strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
- \strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
- \strong{PZA}: pyrazinamide (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK01}{J04AK01}),
- \strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
- \strong{RIB}: rifabutin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB04}{J04AB04}),
- \strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
- \strong{RFP}: rifapentine (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB05}{J04AB05}),
- \strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
- \strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
- \strong{STH}: streptomycin-high (no ATC code),
- \strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
- \strong{TLV}: telavancin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA03}{J01XA03}),
- \strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
- \strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
- \strong{TCC}: ticarcillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR03}{J01CR03}),
- \strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
- \strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
- \strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
- \strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
- \strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
+\strong{AMK}: amikacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB06}{J01GB06}),
+\strong{AMX}: amoxicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA04}{J01CA04}),
+\strong{AMC}: amoxicillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR02}{J01CR02}),
+\strong{AMP}: ampicillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA01}{J01CA01}),
+\strong{SAM}: ampicillin/sulbactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR01}{J01CR01}),
+\strong{AZM}: azithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA10}{J01FA10}),
+\strong{AZL}: azlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA09}{J01CA09}),
+\strong{ATM}: aztreonam (\href{https://www.whocc.no/atc_ddd_index/?code=J01DF01}{J01DF01}),
+\strong{CAP}: capreomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB30}{J04AB30}),
+\strong{RID}: cefaloridine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB02}{J01DB02}),
+\strong{CZO}: cefazolin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB04}{J01DB04}),
+\strong{FEP}: cefepime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DE01}{J01DE01}),
+\strong{CTX}: cefotaxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD01}{J01DD01}),
+\strong{CTT}: cefotetan (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC05}{J01DC05}),
+\strong{FOX}: cefoxitin (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC01}{J01DC01}),
+\strong{CPT}: ceftaroline (\href{https://www.whocc.no/atc_ddd_index/?code=J01DI02}{J01DI02}),
+\strong{CAZ}: ceftazidime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD02}{J01DD02}),
+\strong{CRO}: ceftriaxone (\href{https://www.whocc.no/atc_ddd_index/?code=J01DD04}{J01DD04}),
+\strong{CXM}: cefuroxime (\href{https://www.whocc.no/atc_ddd_index/?code=J01DC02}{J01DC02}),
+\strong{CED}: cephradine (\href{https://www.whocc.no/atc_ddd_index/?code=J01DB09}{J01DB09}),
+\strong{CHL}: chloramphenicol (\href{https://www.whocc.no/atc_ddd_index/?code=J01BA01}{J01BA01}),
+\strong{CIP}: ciprofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA02}{J01MA02}),
+\strong{CLR}: clarithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA09}{J01FA09}),
+\strong{CLI}: clindamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF01}{J01FF01}),
+\strong{COL}: colistin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB01}{J01XB01}),
+\strong{DAP}: daptomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX09}{J01XX09}),
+\strong{DOR}: doripenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH04}{J01DH04}),
+\strong{DOX}: doxycycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA02}{J01AA02}),
+\strong{ETP}: ertapenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH03}{J01DH03}),
+\strong{ERY}: erythromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA01}{J01FA01}),
+\strong{ETH}: ethambutol (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK02}{J04AK02}),
+\strong{FLC}: flucloxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF05}{J01CF05}),
+\strong{FOS}: fosfomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX01}{J01XX01}),
+\strong{FUS}: fusidic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XC01}{J01XC01}),
+\strong{GAT}: gatifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA16}{J01MA16}),
+\strong{GEN}: gentamicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB03}{J01GB03}),
+\strong{GEH}: gentamicin-high (no ATC code),
+\strong{IPM}: imipenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH51}{J01DH51}),
+\strong{INH}: isoniazid (\href{https://www.whocc.no/atc_ddd_index/?code=J04AC01}{J04AC01}),
+\strong{KAN}: kanamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB04}{J01GB04}),
+\strong{LVX}: levofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA12}{J01MA12}),
+\strong{LIN}: lincomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FF02}{J01FF02}),
+\strong{LNZ}: linezolid (\href{https://www.whocc.no/atc_ddd_index/?code=J01XX08}{J01XX08}),
+\strong{MEM}: meropenem (\href{https://www.whocc.no/atc_ddd_index/?code=J01DH02}{J01DH02}),
+\strong{MTR}: metronidazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01XD01}{J01XD01}),
+\strong{MEZ}: mezlocillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA10}{J01CA10}),
+\strong{MNO}: minocycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA08}{J01AA08}),
+\strong{MFX}: moxifloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA14}{J01MA14}),
+\strong{NAL}: nalidixic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01MB02}{J01MB02}),
+\strong{NEO}: neomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB05}{J01GB05}),
+\strong{NET}: netilmicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB07}{J01GB07}),
+\strong{NIT}: nitrofurantoin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XE01}{J01XE01}),
+\strong{NOR}: norfloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA06}{J01MA06}),
+\strong{NOV}: novobiocin (\href{https://www.whocc.no/atc_ddd_index/?code=QJ01XX95}{QJ01XX95}),
+\strong{OFX}: ofloxacin (\href{https://www.whocc.no/atc_ddd_index/?code=J01MA01}{J01MA01}),
+\strong{OXA}: oxacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CF04}{J01CF04}),
+\strong{PEN}: penicillin G (\href{https://www.whocc.no/atc_ddd_index/?code=J01CE01}{J01CE01}),
+\strong{PIP}: piperacillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA12}{J01CA12}),
+\strong{TZP}: piperacillin/tazobactam (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR05}{J01CR05}),
+\strong{PLB}: polymyxin B (\href{https://www.whocc.no/atc_ddd_index/?code=J01XB02}{J01XB02}),
+\strong{PRI}: pristinamycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG01}{J01FG01}),
+\strong{PZA}: pyrazinamide (\href{https://www.whocc.no/atc_ddd_index/?code=J04AK01}{J04AK01}),
+\strong{QDA}: quinupristin/dalfopristin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FG02}{J01FG02}),
+\strong{RIB}: rifabutin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB04}{J04AB04}),
+\strong{RIF}: rifampicin (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB02}{J04AB02}),
+\strong{RFP}: rifapentine (\href{https://www.whocc.no/atc_ddd_index/?code=J04AB05}{J04AB05}),
+\strong{RXT}: roxithromycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01FA06}{J01FA06}),
+\strong{SIS}: sisomicin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB08}{J01GB08}),
+\strong{STH}: streptomycin-high (no ATC code),
+\strong{TEC}: teicoplanin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA02}{J01XA02}),
+\strong{TLV}: telavancin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA03}{J01XA03}),
+\strong{TCY}: tetracycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA07}{J01AA07}),
+\strong{TIC}: ticarcillin (\href{https://www.whocc.no/atc_ddd_index/?code=J01CA13}{J01CA13}),
+\strong{TCC}: ticarcillin/clavulanic acid (\href{https://www.whocc.no/atc_ddd_index/?code=J01CR03}{J01CR03}),
+\strong{TGC}: tigecycline (\href{https://www.whocc.no/atc_ddd_index/?code=J01AA12}{J01AA12}),
+\strong{TOB}: tobramycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01GB01}{J01GB01}),
+\strong{TMP}: trimethoprim (\href{https://www.whocc.no/atc_ddd_index/?code=J01EA01}{J01EA01}),
+\strong{SXT}: trimethoprim/sulfamethoxazole (\href{https://www.whocc.no/atc_ddd_index/?code=J01EE01}{J01EE01}),
+\strong{VAN}: vancomycin (\href{https://www.whocc.no/atc_ddd_index/?code=J01XA01}{J01XA01}).
 }
 
 \section{Interpretation of S, I and R}{
 
 In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
 \itemize{
-  \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
-  \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
-  \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
+\item \strong{S} - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+\item \strong{I} - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
+\item \strong{R} - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
 }
 
 Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
 
-This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates.
+This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
 }
 
 \section{Read more on our website!}{
diff --git a/man/microorganisms.Rd b/man/microorganisms.Rd
index f8f1fbb15..34c0dc79e 100755
--- a/man/microorganisms.Rd
+++ b/man/microorganisms.Rd
@@ -5,38 +5,38 @@
 \alias{microorganisms}
 \title{Data set with ~70,000 microorganisms}
 \format{A \code{\link{data.frame}} with 69,447 observations and 16 variables:
-\describe{
-  \item{\code{mo}}{ID of microorganism as used by this package}
-  \item{\code{col_id}}{Catalogue of Life ID}
-  \item{\code{fullname}}{Full name, like \code{"Escherichia coli"}}
-  \item{\code{kingdom}, \code{phylum}, \code{class}, \code{order}, \code{family}, \code{genus}, \code{species}, \code{subspecies}}{Taxonomic rank of the microorganism}
-  \item{\code{rank}}{Text of the taxonomic rank of the microorganism, like \code{"species"} or \code{"genus"}}
-  \item{\code{ref}}{Author(s) and year of concerning scientific publication}
-  \item{\code{species_id}}{ID of the species as used by the Catalogue of Life}
-  \item{\code{source}}{Either "CoL", "DSMZ" (see Source) or "manually added"}
-  \item{\code{prevalence}}{Prevalence of the microorganism, see \code{?as.mo}}
+\itemize{
+\item \code{mo}\cr ID of microorganism as used by this package
+\item \code{col_id}\cr Catalogue of Life ID
+\item \code{fullname}\cr Full name, like \code{"Escherichia coli"}
+\item \code{kingdom}, \code{phylum}, \code{class}, \code{order}, \code{family}, \code{genus}, \code{species}, \code{subspecies}\cr Taxonomic rank of the microorganism
+\item \code{rank}\cr Text of the taxonomic rank of the microorganism, like \code{"species"} or \code{"genus"}
+\item \code{ref}\cr Author(s) and year of concerning scientific publication
+\item \code{species_id}\cr ID of the species as used by the Catalogue of Life
+\item \code{source}\cr Either "CoL", "DSMZ" (see Source) or "manually added"
+\item \code{prevalence}\cr Prevalence of the microorganism, see \code{\link[=as.mo]{as.mo()}}
 }}
 \source{
-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: 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()}}).
 
-Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, \url{http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date} (check included version with \code{\link{catalogue_of_life_version}()}).
+Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, \url{http://www.dsmz.de/bacterial-diversity/prokaryotic-nomenclature-up-to-date} (check included version with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}).
 }
 \usage{
 microorganisms
 }
 \description{
-A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link{as.mo}}.
+A data set containing the microbial taxonomy of six kingdoms from the Catalogue of Life. MO codes can be looked up using \code{\link[=as.mo]{as.mo()}}.
 }
 \details{
 Manually added were:
 \itemize{
-  \item{11 entries of \emph{Streptococcus} (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)}
-  \item{2 entries of \emph{Staphylococcus} (coagulase-negative [CoNS] and coagulase-positive [CoPS])}
-  \item{3 entries of \emph{Trichomonas} (\emph{Trichomonas vaginalis}, and its family and genus)}
-  \item{1 entry of \emph{Blastocystis} (\emph{Blastocystis hominis}), although it officially does not exist (Noel \emph{et al.} 2005, PMID 15634993)}
-  \item{5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)}
-  \item{6 families under the Enterobacterales order, according to Adeolu \emph{et al.} (2016, PMID 27620848), that are not in the Catalogue of Life}
-  \item{12,600 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications}
+\item 11 entries of \emph{Streptococcus} (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
+\item 2 entries of \emph{Staphylococcus} (coagulase-negative \link{CoNS} and coagulase-positive \link{CoPS})
+\item 3 entries of \emph{Trichomonas} (\emph{Trichomonas vaginalis}, and its family and genus)
+\item 1 entry of \emph{Blastocystis} (\emph{Blastocystis hominis}), although it officially does not exist (Noel \emph{et al.} 2005, PMID 15634993)
+\item 5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)
+\item 6 families under the Enterobacterales order, according to Adeolu \emph{et al.} (2016, PMID 27620848), that are not in the Catalogue of Life
+\item 12,600 species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications
 }
 }
 \section{About the records from DSMZ (see source)}{
@@ -51,7 +51,7 @@ From: \url{https://www.dsmz.de/support/bacterial-nomenclature-up-to-date-downloa
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Read more on our website!}{
@@ -60,6 +60,6 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{as.mo}}, \code{\link{mo_property}}, \code{\link{microorganisms.codes}}
+\code{\link[=as.mo]{as.mo()}}, \code{\link[=mo_property]{mo_property()}}, \link{microorganisms.codes}
 }
 \keyword{datasets}
diff --git a/man/microorganisms.codes.Rd b/man/microorganisms.codes.Rd
index 68932fa87..e3b8fdd66 100644
--- a/man/microorganisms.codes.Rd
+++ b/man/microorganisms.codes.Rd
@@ -5,22 +5,22 @@
 \alias{microorganisms.codes}
 \title{Translation table for common microorganism codes}
 \format{A \code{\link{data.frame}} with 5,433 observations and 2 variables:
-\describe{
-  \item{\code{code}}{Commonly used code of a microorganism}
-  \item{\code{mo}}{ID of the microorganism in the \code{\link{microorganisms}} data set}
+\itemize{
+\item \code{code}\cr Commonly used code of a microorganism
+\item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set
 }}
 \usage{
 microorganisms.codes
 }
 \description{
-A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with \code{\link{set_mo_source}}.
+A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with \code{\link[=set_mo_source]{set_mo_source()}}.
 }
 \section{Catalogue of Life}{
 
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Read more on our website!}{
@@ -29,6 +29,6 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{as.mo}} \code{\link{microorganisms}}
+\code{\link[=as.mo]{as.mo()}} \link{microorganisms}
 }
 \keyword{datasets}
diff --git a/man/microorganisms.old.Rd b/man/microorganisms.old.Rd
index 17085c87c..4031d01c0 100644
--- a/man/microorganisms.old.Rd
+++ b/man/microorganisms.old.Rd
@@ -5,28 +5,28 @@
 \alias{microorganisms.old}
 \title{Data set with previously accepted taxonomic names}
 \format{A \code{\link{data.frame}} with 24,246 observations and 5 variables:
-\describe{
-  \item{\code{col_id}}{Catalogue of Life ID that was originally given}
-  \item{\code{col_id_new}}{New Catalogue of Life ID that responds to an entry in the \code{\link{microorganisms}} data set}
-  \item{\code{fullname}}{Old full taxonomic name of the microorganism}
-  \item{\code{ref}}{Author(s) and year of concerning scientific publication}
-  \item{\code{prevalence}}{Prevalence of the microorganism, see \code{?as.mo}}
+\itemize{
+\item \code{col_id}\cr Catalogue of Life ID that was originally given
+\item \code{col_id_new}\cr New Catalogue of Life ID that responds to an entry in the \link{microorganisms} data set
+\item \code{fullname}\cr Old full taxonomic name of the microorganism
+\item \code{ref}\cr Author(s) and year of concerning scientific publication
+\item \code{prevalence}\cr Prevalence of the microorganism, see \code{\link[=as.mo]{as.mo()}}
 }}
 \source{
-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: 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()}}).
 }
 \usage{
 microorganisms.old
 }
 \description{
-A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by \code{\link{as.mo}}.
+A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by \code{\link[=as.mo]{as.mo()}}.
 }
 \section{Catalogue of Life}{
 
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Read more on our website!}{
@@ -35,6 +35,6 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms}}
+\code{\link[=as.mo]{as.mo()}} \code{\link[=mo_property]{mo_property()}} \link{microorganisms}
 }
 \keyword{datasets}
diff --git a/man/mo_property.Rd b/man/mo_property.Rd
index 457a17fe4..08a5949a7 100644
--- a/man/mo_property.Rd
+++ b/man/mo_property.Rd
@@ -70,58 +70,57 @@ mo_url(x, open = FALSE, ...)
 mo_property(x, property = "fullname", language = get_locale(), ...)
 }
 \arguments{
-\item{x}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.mo}}}
+\item{x}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
-\item{...}{other parameters passed on to \code{\link{as.mo}}}
+\item{...}{other parameters passed on to \code{\link[=as.mo]{as.mo()}}}
 
-\item{open}{browse the URL using \code{\link[utils]{browseURL}()}}
+\item{open}{browse the URL using \code{\link[utils:browseURL]{utils::browseURL()}}}
 
-\item{property}{one of the column names of the \code{\link{microorganisms}} data set or \code{"shortname"}}
+\item{property}{one of the column names of the \link{microorganisms} data set or \code{"shortname"}}
 }
 \value{
 \itemize{
-  \item{An \code{integer} in case of \code{mo_year}}
-  \item{A \code{list} in case of \code{mo_taxonomy}}
-  \item{A named \code{character} in case of \code{mo_url}}
-  \item{A \code{character} in all other cases}
+\item An \code{\link{integer}} in case of \code{\link[=mo_year]{mo_year()}}
+\item A \code{\link{list}} in case of \code{\link[=mo_taxonomy]{mo_taxonomy()}}
+\item A named \code{\link{character}} in case of \code{\link[=mo_url]{mo_url()}}
+\item A \code{\link{character}} in all other cases
 }
 }
 \description{
-Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with \code{\link{as.mo}}, which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples.
+Use these functions to return a specific property of a microorganism. All input values will be evaluated internally with \code{\link[=as.mo]{as.mo()}}, which makes it possible for input of these functions to use microbial abbreviations, codes and names. See Examples.
 }
 \details{
-All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for \code{mo_ref}, \code{mo_authors} and \code{mo_year}. This leads to the following results:
+All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for \code{\link[=mo_ref]{mo_ref()}}, \code{\link[=mo_authors]{mo_authors()}} and \code{\link[=mo_year]{mo_year()}}. This leads to the following results:
 \itemize{
-  \item{\code{mo_name("Chlamydia psittaci")} will return \code{"Chlamydophila psittaci"} (with a warning about the renaming)}
-  \item{\code{mo_ref("Chlamydia psittaci")} will return \code{"Page, 1968"} (with a warning about the renaming)}
-  \item{\code{mo_ref("Chlamydophila psittaci")} will return \code{"Everett et al., 1999"} (without a warning)}
+\item \code{mo_name("Chlamydia psittaci")} will return \code{"Chlamydophila psittaci"} (with a warning about the renaming)
+\item \code{mo_ref("Chlamydia psittaci")} will return \code{"Page, 1968"} (with a warning about the renaming)
+\item \code{mo_ref("Chlamydophila psittaci")} will return \code{"Everett et al., 1999"} (without a warning)
 }
 
-The Gram stain - \code{mo_gramstain()} - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value \code{NA}.
+The Gram stain - \code{\link[=mo_gramstain]{mo_gramstain()}} - will be determined on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002) who defined subkingdoms Negibacteria and Posibacteria, only these phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. These bacteria are considered Gram positive - all other bacteria are considered Gram negative. Species outside the kingdom of Bacteria will return a value \code{NA}.
 
 All output will be \link{translate}d where possible.
 
-The function \code{mo_url()} will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
+The function \code{\link[=mo_url]{mo_url()}} will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
 }
 \section{Catalogue of Life}{
 
 \if{html}{\figure{logo_col.png}{options: height=40px style=margin-bottom:5px} \cr}
 This package contains the complete taxonomic tree of almost all microorganisms (~70,000 species) from the authoritative and comprehensive Catalogue of Life (\url{http://www.catalogueoflife.org}). The Catalogue of Life is the most comprehensive and authoritative global index of species currently available.
 
-\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link{catalogue_of_life_version}()}.
+\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which version of the Catalogue of Life was included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
 }
 
 \section{Source}{
 
-[1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
-
-[2] 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. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
-
-[3] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
-
-[4] 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}()}).
+\enumerate{
+\item Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
+\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. \url{https://doi.org/10.1016/j.cmi.2019.02.028}
+\item Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/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()}}).
+}
 }
 
 \section{Read more on our website!}{
@@ -209,10 +208,10 @@ mo_fullname("S. pyogenes",
 
 # get a list with the complete taxonomy (from kingdom to subspecies)
 mo_taxonomy("E. coli")
-# get a list with the taxonomy, the authors and the URL to the online database
+# get a list with the taxonomy, the authors, Gram-stain and URL to the online database
 mo_info("E. coli")
 }
 }
 \seealso{
-\code{\link{microorganisms}}
+\link{microorganisms}
 }
diff --git a/man/mo_source.Rd b/man/mo_source.Rd
index 9a51ae7b3..30fc5effc 100644
--- a/man/mo_source.Rd
+++ b/man/mo_source.Rd
@@ -14,24 +14,21 @@ get_mo_source()
 \item{path}{location of your reference file, see Details}
 }
 \description{
-These functions can be used to predefine your own reference to be used in \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}.
+These functions can be used to predefine your own reference to be used in \code{\link[=as.mo]{as.mo()}} and consequently all \verb{mo_*} functions like \code{\link[=mo_genus]{mo_genus()}} and \code{\link[=mo_gramstain]{mo_gramstain()}}.
 
 This is \strong{the fastest way} to have your organisation (or analysis) specific codes picked up and translated by this package.
 }
 \details{
 The reference file can be a text file seperated with commas (CSV) or tabs or pipes, an Excel file (either 'xls' or 'xlsx' format) or an R object file (extension '.rds'). To use an Excel file, you need to have the \code{readxl} package installed.
 
-\code{set_mo_source} will check the file for validity: it must be a \code{data.frame}, must have a column named \code{"mo"} which contains values from \code{microorganisms$mo} and must have a reference column with your own defined values. If all tests pass, \code{set_mo_source} will read the file into R and export it to \code{"~/.mo_source.rds"}. This compressed data file will then be used at default for MO determination (function \code{\link{as.mo}} and consequently all \code{mo_*} functions like \code{\link{mo_genus}} and \code{\link{mo_gramstain}}). The location of the original file will be saved as option with \code{\link{options}(mo_source = path)}. Its timestamp will be saved with \code{\link{options}(mo_source_datetime = ...)}.
+\code{\link[=set_mo_source]{set_mo_source()}} will check the file for validity: it must be a \code{\link{data.frame}}, must have a column named \code{"mo"} which contains values from \code{\link[=microorganisms]{microorganisms$mo}} and must have a reference column with your own defined values. If all tests pass, \code{\link[=set_mo_source]{set_mo_source()}} will read the file into R and export it to \code{"~/.mo_source.rds"}. This compressed data file will then be used at default for MO determination (function \code{\link[=as.mo]{as.mo()}} and consequently all \verb{mo_*} functions like \code{\link[=mo_genus]{mo_genus()}} and \code{\link[=mo_gramstain]{mo_gramstain()}}). The location of the original file will be saved as option with \code{options(mo_source = path)}. Its timestamp will be saved with \code{options(mo_source_datetime = ...)}.
 
-\code{get_mo_source} will return the data set by reading \code{"~/.mo_source.rds"} with \code{\link{readRDS}}. If the original file has changed (the file defined with \code{path}), it will call \code{set_mo_source} to update the data file automatically.
+\code{\link[=get_mo_source]{get_mo_source()}} will return the data set by reading \code{"~/.mo_source.rds"} with \code{\link[=readRDS]{readRDS()}}. If the original file has changed (the file defined with \code{path}), it will call \code{\link[=set_mo_source]{set_mo_source()}} to update the data file automatically.
 
-Reading an Excel file (\code{.xlsx}) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by \code{get_mo_source} in only a couple of microseconds (a millionth of a second).
-}
-\section{How it works}{
+Reading an Excel file (\code{.xlsx}) with only one row has a size of 8-9 kB. The compressed file used by this package will have a size of 0.1 kB and can be read by \code{\link[=get_mo_source]{get_mo_source()}} in only a couple of microseconds (a millionth of a second).
+\subsection{How it works}{
 
-Imagine this data on a sheet of an Excel file (mo codes were looked up in the `microorganisms` data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:
-\preformatted{
-  |         A          |      B      |
+Imagine this data on a sheet of an Excel file (mo codes were looked up in the \code{microorganisms} data set). The first column contains the organisation specific codes, the second column contains an MO code from this package:\preformatted{  |         A          |      B      |
 --|--------------------|-------------|
 1 | Organisation XYZ   | mo          |
 2 | lab_mo_ecoli       | B_ESCHR_COL |
@@ -39,56 +36,46 @@ Imagine this data on a sheet of an Excel file (mo codes were looked up in the `m
 4 |                    |             |
 }
 
-We save it as \code{'home/me/ourcodes.xlsx'}. Now we have to set it as a source:
-\preformatted{
-set_mo_source("home/me/ourcodes.xlsx")
+We save it as \code{"home/me/ourcodes.xlsx"}. Now we have to set it as a source:\preformatted{set_mo_source("home/me/ourcodes.xlsx")
 # Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
 }
 
-It has now created a file "~/.mo_source.rds" with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept.
+It has now created a file \code{"~/.mo_source.rds"} with the contents of our Excel file, but only the first column with foreign values and the 'mo' column will be kept.
 
-And now we can use it in our functions:
-\preformatted{
-as.mo("lab_mo_ecoli")
-[1] B_ESCHR_COL
+And now we can use it in our functions:\preformatted{as.mo("lab_mo_ecoli")
+\[1\] B_ESCHR_COLI
 
 mo_genus("lab_mo_kpneumoniae")
 [1] "Klebsiella"
 
 # other input values still work too
 as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
-[1] B_ESCHR_COL  B_ESCHR_COL  B_ESCHR_COL
+[1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
 }
 
-If we edit the Excel file to, let's say, this:
-\preformatted{
-  |         A          |      B      |
---|--------------------|-------------|
-1 | Organisation XYZ   | mo          |
-2 | lab_mo_ecoli       | B_ESCHR_COL |
-3 | lab_mo_kpneumoniae | B_KLBSL_PNE |
-4 | lab_Staph_aureus   | B_STPHY_AUR |
-5 |                    |             |
+If we edit the Excel file to, let's say, this:\preformatted{  |         A          |       B      |
+--|--------------------|--------------|
+1 | Organisation XYZ   | mo           |
+2 | lab_mo_ecoli       | B_ESCHR_COLI |
+3 | lab_mo_kpneumoniae | B_KLBSL_PNMN |
+4 | lab_Staph_aureus   | B_STPHY_AURS |
+5 |                    |              |
 }
 
-...any new usage of an MO function in this package will update your data:
-\preformatted{
-as.mo("lab_mo_ecoli")
+...any new usage of an MO function in this package will update your data:\preformatted{as.mo("lab_mo_ecoli")
 # Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'.
-[1] B_ESCHR_COL
+[1] B_ESCHR_COLI
 
 mo_genus("lab_Staph_aureus")
 [1] "Staphylococcus"
 }
 
-To remove the reference completely, just use any of these:
-\preformatted{
-set_mo_source("")
+To remove the reference completely, just use any of these:\preformatted{set_mo_source("")
 set_mo_source(NULL)
 # Removed mo_source file '~/.mo_source.rds'.
 }
 }
-
+}
 \section{Read more on our website!}{
 
 On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
diff --git a/man/p_symbol.Rd b/man/p_symbol.Rd
index 8ed48cb9a..e432200da 100644
--- a/man/p_symbol.Rd
+++ b/man/p_symbol.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/p_symbol.R
 \name{p_symbol}
 \alias{p_symbol}
-\title{Symbol of a p value}
+\title{Symbol of a p-value}
 \usage{
 p_symbol(p, emptychar = " ")
 }
@@ -15,7 +15,7 @@ p_symbol(p, emptychar = " ")
 Text
 }
 \description{
-Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
+Return the symbol related to the p-value: 0 '\verb{***}' 0.001 '\verb{**}' 0.01 '\code{*}' 0.05 '\code{.}' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
 }
 \section{Read more on our website!}{
 
diff --git a/man/proportion.Rd b/man/proportion.Rd
index 6d605ad94..7b4f46703 100644
--- a/man/proportion.Rd
+++ b/man/proportion.Rd
@@ -17,77 +17,79 @@
 \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
 }
 \usage{
-resistance(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-susceptibility(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_R(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_IR(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_I(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_SI(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_S(..., minimum = 30, as_percent = FALSE,
-  only_all_tested = FALSE)
+proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)
 
-proportion_df(data, translate_ab = "name", language = get_locale(),
-  minimum = 30, as_percent = FALSE, combine_SI = TRUE,
-  combine_IR = FALSE)
+proportion_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  minimum = 30,
+  as_percent = FALSE,
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
 
-rsi_df(data, translate_ab = "name", language = get_locale(),
-  minimum = 30, as_percent = FALSE, combine_SI = TRUE,
-  combine_IR = FALSE)
+rsi_df(
+  data,
+  translate_ab = "name",
+  language = get_locale(),
+  minimum = 30,
+  as_percent = FALSE,
+  combine_SI = TRUE,
+  combine_IR = FALSE
+)
 }
 \arguments{
-\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
+\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link[=as.rsi]{as.rsi()}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
 
 \item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.}
 
 \item{as_percent}{a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
 
-\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}) a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below}
+\item{only_all_tested}{(for combination therapies, i.e. using more than one variable for \code{...}): a logical to indicate that isolates must be tested for all antibiotics, see section \emph{Combination therapy} below}
 
-\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
+\item{data}{a \code{\link{data.frame}} containing columns with class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}
 
-\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
+\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
 
-\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
+\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_locale]{get_locale()}}) and can also be set with \code{getOption("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
 
 \item{combine_SI}{a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.}
 
 \item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}.}
 }
 \value{
-Double or, when \code{as_percent = TRUE}, a character.
+A \code{\link{double}} or, when \code{as_percent = TRUE}, a \code{\link{character}}.
 }
 \description{
-These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{\link[dplyr:summarise]{dplyr::summarise()}} and support grouped variables, please see \emph{Examples}.
 
-\code{resistance()} should be used to calculate resistance, \code{susceptibility()} should be used to calculate susceptibility.\cr
+\code{\link[=resistance]{resistance()}} should be used to calculate resistance, \code{\link[=susceptibility]{susceptibility()}} should be used to calculate susceptibility.\cr
 }
 \details{
-The function \code{resistance()} is equal to the function \code{proportion_R()}. The function \code{susceptibility()} is equal to the function \code{proportion_SI()}.
- 
-\strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link{first_isolate}} to determine them in your data set.
+The function \code{\link[=resistance]{resistance()}} is equal to the function \code{\link[=proportion_R]{proportion_R()}}. The function \code{\link[=susceptibility]{susceptibility()}} is equal to the function \code{\link[=proportion_SI]{proportion_SI()}}.
 
-These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[AMR]{count}} functions to count isolates. The function \code{susceptibility()} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).}
+\strong{Remember that you should filter your table to let it contain only first isolates!} This is needed to exclude duplicates and to reduce selection bias. Use \code{\link[=first_isolate]{first_isolate()}} to determine them in your data set.
 
-The function \code{proportion_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and calculates the proportions R, I and S. The function \code{rsi_df()} works exactly like \code{proportion_df()}, but adds the number of isolates.
+These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the \code{\link[AMR:count]{AMR::count()}} functions to count isolates. The function \code{\link[=susceptibility]{susceptibility()}} is essentially equal to \code{count_susceptible() / count_all()}. \emph{Low counts can infuence the outcome - the \code{proportion} functions may camouflage this, since they only return the proportion (albeit being dependent on the \code{minimum} parameter).}
+
+The function \code{\link[=proportion_df]{proportion_df()}} takes any variable from \code{data} that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) and calculates the proportions R, I and S. The function \code{\link[=rsi_df]{rsi_df()}} works exactly like \code{\link[=proportion_df]{proportion_df()}}, but adds the number of isolates.
 }
 \section{Combination therapy}{
 
-When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{susceptibility} works to calculate the \%SI:
-
-\preformatted{
---------------------------------------------------------------------
+When using more than one variable for \code{...} (= combination therapy)), use \code{only_all_tested} to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how \code{\link[=susceptibility]{susceptibility()}} works to calculate the \%SI:\preformatted{--------------------------------------------------------------------
                     only_all_tested = FALSE  only_all_tested = TRUE
                     -----------------------  -----------------------
  Drug A    Drug B   include as  include as   include as  include as
@@ -105,14 +107,11 @@ When using more than one variable for \code{...} (= combination therapy)), use \
 --------------------------------------------------------------------
 }
 
-Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:
-\preformatted{
-    count_S()    +   count_I()    +   count_R()    = count_all()
+Please note that, in combination therapies, for \code{only_all_tested = TRUE} applies that:\preformatted{    count_S()    +   count_I()    +   count_R()    = count_all()
   proportion_S() + proportion_I() + proportion_R() = 1
 }
-and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:
-\preformatted{
-    count_S()    +   count_I()    +   count_R()    >= count_all()
+
+and that, in combination therapies, for \code{only_all_tested = FALSE} applies that:\preformatted{    count_S()    +   count_I()    +   count_R()    >= count_all()
   proportion_S() + proportion_I() + proportion_R() >= 1
 }
 
@@ -122,16 +121,15 @@ Using \code{only_all_tested} has no impact when only using one antibiotic as inp
 \section{Interpretation of S, I and R}{
 
 In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
 \itemize{
-  \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
-  \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
-  \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
+\item \strong{S} - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+\item \strong{I} - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
+\item \strong{R} - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
 }
 
 Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
 
-This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates.
+This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
 }
 
 \section{Read more on our website!}{
@@ -222,5 +220,5 @@ my_table \%>\%
 }
 }
 \seealso{
-\code{\link[AMR]{count}_*} to count resistant and susceptible isolates.
+\code{\link[AMR:count]{AMR::count()}} to count resistant and susceptible isolates.
 }
diff --git a/man/read.4D.Rd b/man/read.4D.Rd
index f6b7fcf39..319c5779c 100644
--- a/man/read.4D.Rd
+++ b/man/read.4D.Rd
@@ -4,11 +4,24 @@
 \alias{read.4D}
 \title{Read data from 4D database}
 \usage{
-read.4D(file, info = interactive(), header = TRUE, row.names = NULL,
-  sep = "\\t", quote = "\\"'", dec = ",", na.strings = c("NA", "",
-  "."), skip = 2, check.names = TRUE, strip.white = TRUE,
-  fill = TRUE, blank.lines.skip = TRUE, stringsAsFactors = FALSE,
-  fileEncoding = "UTF-8", encoding = "UTF-8")
+read.4D(
+  file,
+  info = interactive(),
+  header = TRUE,
+  row.names = NULL,
+  sep = "\\t",
+  quote = "\\"'",
+  dec = ",",
+  na.strings = c("NA", "", "."),
+  skip = 2,
+  check.names = TRUE,
+  strip.white = TRUE,
+  fill = TRUE,
+  blank.lines.skip = TRUE,
+  stringsAsFactors = FALSE,
+  fileEncoding = "UTF-8",
+  encoding = "UTF-8"
+)
 }
 \arguments{
 \item{file}{the name of the file which the data are to be read from.
@@ -117,7 +130,7 @@ read.4D(file, info = interactive(), header = TRUE, row.names = NULL,
   }
 }
 \description{
-This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with \code{\link{as.mo}} and transform all antimicrobial columns with \code{\link{as.rsi}}.
+This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the MO codes with \code{\link[=as.mo]{as.mo()}} and transform all antimicrobial columns with \code{\link[=as.rsi]{as.rsi()}}.
 }
 \details{
 Column names will be transformed, but the original column names are set as a "label" attribute and can be seen in e.g. RStudio Viewer.
diff --git a/man/resistance_predict.Rd b/man/resistance_predict.Rd
index c29ef00cd..0a8a2d1bf 100644
--- a/man/resistance_predict.Rd
+++ b/man/resistance_predict.Rd
@@ -7,24 +7,49 @@
 \alias{ggplot_rsi_predict}
 \title{Predict antimicrobial resistance}
 \usage{
-resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,
-  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
-  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
+resistance_predict(
+  x,
+  col_ab,
+  col_date = NULL,
+  year_min = NULL,
+  year_max = NULL,
+  year_every = 1,
+  minimum = 30,
+  model = NULL,
+  I_as_S = TRUE,
+  preserve_measurements = TRUE,
+  info = TRUE,
+  ...
+)
 
-rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
-  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
-  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)
+rsi_predict(
+  x,
+  col_ab,
+  col_date = NULL,
+  year_min = NULL,
+  year_max = NULL,
+  year_every = 1,
+  minimum = 30,
+  model = NULL,
+  I_as_S = TRUE,
+  preserve_measurements = TRUE,
+  info = TRUE,
+  ...
+)
 
-\method{plot}{resistance_predict}(x,
-  main = paste("Resistance Prediction of", x_name), ...)
+\method{plot}{resistance_predict}(x, main = paste("Resistance Prediction of", x_name), ...)
 
-ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
-  ribbon = TRUE, ...)
+ggplot_rsi_predict(
+  x,
+  main = paste("Resistance Prediction of", x_name),
+  ribbon = TRUE,
+  ...
+)
 }
 \arguments{
-\item{x}{a \code{data.frame} containing isolates.}
+\item{x}{a \code{\link{data.frame}} containing isolates.}
 
-\item{col_ab}{column name of \code{x} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
+\item{col_ab}{column name of \code{x} containing antimicrobial interpretations (\code{"R"}, \code{"I"} and \code{"S"})}
 
 \item{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}
 
@@ -36,13 +61,13 @@ ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
 
 \item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
 
-\item{model}{the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options.}
+\item{model}{the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options.}
 
-\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R}). The default, \code{TRUE}, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below.}
+\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R}). The default, \code{TRUE}, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section \emph{Interpretation of S, I and R} below.}
 
 \item{preserve_measurements}{a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.}
 
-\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
+\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link[=summary]{summary()}} of the statistical model.}
 
 \item{...}{parameters passed on to functions}
 
@@ -51,42 +76,42 @@ ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
 \item{ribbon}{a logical to indicate whether a ribbon should be shown (default) or error bars}
 }
 \value{
-\code{data.frame} with extra class \code{"resistance_predict"} with columns:
+A \code{\link{data.frame}} with extra class \code{\link{resistance_predict}} with columns:
 \itemize{
-  \item{\code{year}}
-  \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
-  \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)}
-  \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)}
-  \item{\code{observations}, the total number of available observations in that year, i.e. S + I + R}
-  \item{\code{observed}, the original observed resistant percentages}
-  \item{\code{estimated}, the estimated resistant percentages, calculated by the model}
+\item \code{year}
+\item \code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise
+\item \code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)
+\item \code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)
+\item \code{observations}, the total number of available observations in that year, i.e. \eqn{S + I + R}
+\item \code{observed}, the original observed resistant percentages
+\item \code{estimated}, the estimated resistant percentages, calculated by the model
 }
-Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples.
+
+Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, please see \emph{Examples}.
 }
 \description{
-Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
+Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See \emph{Examples} for a real live example.
 }
 \details{
-Valid options for the statistical model are:
+Valid options for the statistical model (parameter \code{model}) are:
 \itemize{
-  \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
-  \item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution}
-  \item{\code{"lin"} or \code{"linear"}: a linear regression model}
+\item \code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution
+\item \code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution
+\item \code{"lin"} or \code{"linear"}: a linear regression model
 }
 }
 \section{Interpretation of S, I and R}{
 
 In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (\url{http://www.eucast.org/newsiandr/}). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
 \itemize{
-  \item{\strong{S} - }{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
-  \item{\strong{I} - }{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
-  \item{\strong{R} - }{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
+\item \strong{S} - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+\item \strong{I} - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
+\item \strong{R} - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
 }
 
 Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
 
-This AMR package honours this new insight. Use \code{\link{susceptibility}()} (equal to \code{\link{proportion_SI}()}) to determine antimicrobial susceptibility and \code{\link{count_susceptible}()} (equal to \code{\link{count_SI}()}) to count susceptible isolates.
+This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
 }
 
 \section{Read more on our website!}{
@@ -95,7 +120,10 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
-x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial")
+x <- resistance_predict(example_isolates, 
+                        col_ab = "AMX",
+                        year_min = 2010,
+                        model = "binomial")
 plot(x)
 ggplot_rsi_predict(x)
 
@@ -134,13 +162,15 @@ if (!require(ggplot2)) {
     scale_y_continuous(limits = c(0, 1),
                        breaks = seq(0, 1, 0.1),
                        labels = paste0(seq(0, 100, 10), "\%")) +
-    labs(title = expression(paste("Forecast of amoxicillin resistance in ",
+    labs(title = expression(paste("Forecast of Amoxicillin Resistance in ",
                                   italic("E. coli"))),
-         y = "\%IR",
+         y = "\%R",
          x = "Year") +
     theme_minimal(base_size = 13)
 }
 }
 \seealso{
-The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
+The \code{\link[=proportion]{proportion()}} functions to calculate resistance
+
+Models: \code{\link[=lm]{lm()}} \code{\link[=glm]{glm()}}
 }
diff --git a/man/rsi_translation.Rd b/man/rsi_translation.Rd
index 2c558b744..673e03e26 100644
--- a/man/rsi_translation.Rd
+++ b/man/rsi_translation.Rd
@@ -5,22 +5,22 @@
 \alias{rsi_translation}
 \title{Data set for RSI interpretation}
 \format{A \code{\link{data.frame}} with 13,975 observations and 9 variables:
-\describe{
-  \item{\code{guideline}}{Name of the guideline}
-  \item{\code{method}}{Either "MIC" or "DISK"}
-  \item{\code{site}}{Body site, e.g. "Oral" or "Respiratory"}
-  \item{\code{mo}}{Microbial ID, see \code{\link{as.mo}}}
-  \item{\code{ab}}{Antibiotic ID, see \code{\link{as.ab}}}
-  \item{\code{ref_tbl}}{Info about where the guideline rule can be found}
-  \item{\code{disk_dose}}{Dose of the used disk diffusion method}
-  \item{\code{breakpoint_S}}{Lowest MIC value or highest number of millimeters that leads to "S"}
-  \item{\code{breakpoint_R}}{Highest MIC value or lowest number of millimeters that leads to "R"}
+\itemize{
+\item \code{guideline}\cr Name of the guideline
+\item \code{method}\cr Either "MIC" or "DISK"
+\item \code{site}\cr Body site, e.g. "Oral" or "Respiratory"
+\item \code{mo}\cr Microbial ID, see \code{\link[=as.mo]{as.mo()}}
+\item \code{ab}\cr Antibiotic ID, see \code{\link[=as.ab]{as.ab()}}
+\item \code{ref_tbl}\cr Info about where the guideline rule can be found
+\item \code{disk_dose}\cr Dose of the used disk diffusion method
+\item \code{breakpoint_S}\cr Lowest MIC value or highest number of millimeters that leads to "S"
+\item \code{breakpoint_R}\cr Highest MIC value or lowest number of millimeters that leads to "R"
 }}
 \usage{
 rsi_translation
 }
 \description{
-Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link{as.rsi}} to transform MICs or disks measurements to RSI values.
+Data set to interpret MIC and disk diffusion to RSI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2019). Use \code{\link[=as.rsi]{as.rsi()}} to transform MICs or disks measurements to RSI values.
 }
 \section{Read more on our website!}{
 
diff --git a/man/skewness.Rd b/man/skewness.Rd
index a5efcdaf0..e78e00d44 100644
--- a/man/skewness.Rd
+++ b/man/skewness.Rd
@@ -16,7 +16,7 @@ skewness(x, na.rm = FALSE)
 \method{skewness}{data.frame}(x, na.rm = FALSE)
 }
 \arguments{
-\item{x}{a vector of values, a \code{matrix} or a \code{data frame}}
+\item{x}{a vector of values, a \code{\link{matrix}} or a \code{\link{data.frame}}}
 
 \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.}
 }
@@ -31,5 +31,5 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \seealso{
-\code{\link{kurtosis}}
+\code{\link[=kurtosis]{kurtosis()}}
 }
diff --git a/man/translate.Rd b/man/translate.Rd
index 54476b533..d1bbd23bb 100644
--- a/man/translate.Rd
+++ b/man/translate.Rd
@@ -8,7 +8,7 @@
 get_locale()
 }
 \description{
-For language-dependent output of AMR functions, like \code{\link{mo_name}},  \code{\link{mo_type}} and \code{\link{ab_name}}.
+For language-dependent output of AMR functions, like \code{\link[=mo_name]{mo_name()}}, \code{\link[=mo_type]{mo_type()}} and \code{\link[=ab_name]{ab_name()}}.
 }
 \details{
 Strings will be translated to foreign languages if they are defined in a local translation file. Additions to this file can be suggested at our repository. The file can be found here: \url{https://gitlab.com/msberends/AMR/blob/master/data-raw/translations.tsv}.
@@ -17,9 +17,9 @@ Currently supported languages can be found if running: \code{unique(AMR:::transl
 
 Please suggest your own translations \href{https://gitlab.com/msberends/AMR/issues/new?issue[title]=Translation\%20suggestion}{by creating a new issue on our repository}.
 
-This file will be read by all functions where a translated output can be desired, like all \code{\link{mo_property}} functions (\code{\link{mo_fullname}}, \code{\link{mo_type}}, etc.).
+This file will be read by all functions where a translated output can be desired, like all \code{\link[=mo_property]{mo_property()}} functions (\code{\link[=mo_fullname]{mo_fullname()}}, \code{\link[=mo_type]{mo_type()}}, etc.).
 
-The system language will be used at default, if that language is supported. The system language can be overwritten with \code{\link{getOption}("AMR_locale")}.
+The system language will be used at default, if that language is supported. The system language can be overwritten with \code{Sys.setenv(AMR_locale = yourlanguage)}.
 }
 \section{Read more on our website!}{
 
diff --git a/tests/testthat/test-mo_history.R b/tests/testthat/test-mo_history.R
index 04d83ed57..92f06634b 100644
--- a/tests/testthat/test-mo_history.R
+++ b/tests/testthat/test-mo_history.R
@@ -18,27 +18,3 @@
 # ANY WARRANTY OR LIABILITY.                                           #
 # Visit our website for more info: https://msberends.gitlab.io/AMR.    #
 # ==================================================================== #
-
-# context("mo_history.R")
-#
-# test_that("mo_history works", {
-#   clean_mo_history(force = TRUE)
-#   expect_equal(read_mo_history(force = TRUE),
-#                NULL)
-#
-#   expect_equal(as.character(suppressWarnings(as.mo("testsubject"))), "UNKNOWN")
-#
-#   set_mo_history("testsubject", "B_ESCHR_COL",
-#                  uncertainty_level = translate_allow_uncertain(TRUE),
-#                  force = TRUE)
-#
-#   expect_equal(get_mo_history("testsubject",
-#                               uncertainty_level = translate_allow_uncertain(TRUE),
-#                               force = TRUE),
-#                "B_ESCHR_COL")
-#
-#   expect_equal(as.character(as.mo("testsubject", force_mo_history = TRUE)), "B_ESCHR_COL")
-#
-#   expect_equal(colnames(read_mo_history(force = TRUE)),
-#                c("x", "mo", "uncertainty_level", "package_v"))
-# })

                    
     
     
                    
       x
                    a data.frame containing isolates.
                    a data.frame containing isolates.
                    		
     
                    
     
                    
       col_ab
                    column name of x with antimicrobial interpretations (R, I and S)
                    column name of x containing antimicrobial interpretations ("R", "I" and "S")
                    		
     
                    
     
                    
       col_date
                    
     
                    
       model
                    the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using glm(..., family = binomial)), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options.
                    the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options.
                    		
     
                    
     
                    
       I_as_S
                    a logical to indicate whether values I should be treated as S (will otherwise be treated as R). The default, TRUE, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below.
                    a logical to indicate whether values I should be treated as S (will otherwise be treated as R). The default, TRUE, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section Interpretation of S, I and R below.
                    		
     
                    
     
                    
       preserve_measurements
                    
     
                    
       info
                    a logical to indicate whether textual analysis should be printed with the name and summary of the statistical model.
                    a logical to indicate whether textual analysis should be printed with the name and summary() of the statistical model.
                    		
     
                    
     
                    
       ...
     
                    
     
                    
       x
                    a vector of values, a matrix or a data frame
                    a vector of values, a matrix or a data.frame
                    		
     
                    
     
                    
       na.rm

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