v1.7.0

(v1.6.0.9065) unit tests
(v1.6.0.9064) prepare new release
2026-09-11 22:58:52 +02:00 · 2021-05-24 15:29:17 +02:00 · 2021-05-24 11:01:32 +02:00 · 2021-05-24 09:34:08 +02:00 · 2021-05-24 09:00:11 +02:00 · 2021-05-24 00:06:28 +02:00
433 changed files with 124113 additions and 87351 deletions
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,3 +1,4 @@
+^.*\.RData$
 ^.*\.Rproj$
 ^\.Renviron$
 ^\.Rprofile$
@@ -21,10 +22,9 @@
 ^public$
 ^data-raw$
 ^\.lintr$
-^vignettes/AMR.Rmd$
+^tests/testthat/_snaps$
 ^vignettes/benchmarks.Rmd$
 ^vignettes/EUCAST.Rmd$
-^vignettes/MDR.Rmd$
 ^vignettes/PCA.Rmd$
 ^vignettes/resistance_predict.Rmd$
 ^vignettes/SPSS.Rmd$
--- a/.github/workflows/check.yaml
+++ b/.github/workflows/check.yaml
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 on:
@@ -41,92 +41,119 @@ name: R-code-check
 jobs:
  R-code-check:
    runs-on: ${{ matrix.config.os }}
+    
+    continue-on-error: ${{ matrix.config.allowfail }}

-    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+    name: ${{ matrix.config.os }} (R-${{ matrix.config.r }})

    strategy:
      fail-fast: false
      matrix:
        config:
-          - {os: windows-latest, r: 'release'}
-          - {os: macOS-latest,   r: 'release'}
-          - {os: ubuntu-16.04,   r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
-          # - {os: windows-latest, r: 'oldrel'}
-          # - {os: macOS-latest,   r: 'oldrel'}
-          # - {os: ubuntu-16.04,   r: 'oldrel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
-          - {os: windows-latest, r: 'devel'}
-          # - {os: macOS-latest,   r: 'devel'}
-          # - {os: ubuntu-16.04,   r: '4.0', rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
-          # - {os: windows-latest, r: '3.6'}
-          # - {os: ubuntu-16.04,   r: '3.5',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
-          # - {os: ubuntu-16.04,   r: '3.4',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
-          # - {os: ubuntu-16.04,   r: '3.3',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
+          # these are the developmental version of R - we allow those tests to fail
+          - {os: macOS-latest,   r: 'devel',   allowfail: true}
+          - {os: windows-latest, r: 'devel',   allowfail: true}
+          - {os: ubuntu-20.04,   r: 'devel',   allowfail: true,  rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          
+          # these are the current release of R
+          - {os: macOS-latest,   r: 'release', allowfail: false}
+          - {os: windows-latest, r: 'release', allowfail: false}
+          - {os: ubuntu-20.04,   r: 'release', allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          
+          # these are the previous release of R
+          - {os: macOS-latest,   r: 'oldrel',  allowfail: false}
+          - {os: windows-latest, r: 'oldrel',  allowfail: false}
+          - {os: ubuntu-20.04,   r: 'oldrel',  allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          
+          # test against all released versions of R >= 3.0, we support them all!
+          - {os: ubuntu-20.04,   r: '4.1',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '4.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.6',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.5',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.4',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.3',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.2',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.1',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-20.04,   r: '3.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}

    env:
      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
      RSPM: ${{ matrix.config.rspm }}
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

    steps:
      - uses: actions/checkout@v2

-      - uses: r-lib/actions/setup-r@master
+      - uses: r-lib/actions/setup-r@v1
        with:
          r-version: ${{ matrix.config.r }}

-      - uses: r-lib/actions/setup-pandoc@master
-
-      - name: Query dependencies
+      - name: Install Linux dependencies
+        if: runner.os == 'Linux'
+        # update the below with sysreqs::sysreqs("DESCRIPTION") and check the "DEB" entries (for Ubuntu).
+        # we don't want to depend on the sysreqs pkg here, as it requires quite a recent R version
+        # as of May 2021: https://sysreqs.r-hub.io/pkg/AMR,R,cleaner,curl,dplyr,ggplot2,ggtext,knitr,microbenchmark,pillar,readxl,rmarkdown,rstudioapi,rvest,skimr,tidyr,tinytest,xml2,backports,crayon,rlang,vctrs,evaluate,highr,markdown,stringr,yaml,xfun,cli,ellipsis,fansi,lifecycle,utf8,glue,mime,magrittr,stringi,generics,R6,tibble,tidyselect,pkgconfig,purrr,digest,gtable,isoband,MASS,mgcv,scales,withr,nlme,Matrix,farver,labeling,munsell,RColorBrewer,viridisLite,lattice,colorspace,gridtext,Rcpp,RCurl,png,jpeg,bitops,cellranger,progress,rematch,hms,prettyunits,htmltools,jsonlite,tinytex,base64enc,httr,selectr,openssl,askpass,sys,repr,cpp11
        run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
-        shell: Rscript {0}
+          sudo apt install -y libssl-dev pandoc pandoc-citeproc libxml2-dev libicu-dev libcurl4-openssl-dev libpng-dev

-      - name: Cache R packages
+      - name: Restore cached R packages
+        # this step will add the step 'Post Restore cached R packages' on a succesful run
        if: runner.os != 'Windows'
        uses: actions/cache@v1
        with:
          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+          key: ${{ matrix.config.os }}-r-${{ matrix.config.r }}-v4

-      - name: Install system dependencies
-        if: runner.os == 'Linux'
-        env:
-          RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
+      - name: Unpack AMR and install R dependencies
+        if: always()
        run: |
-          Rscript -e "remotes::install_github('r-hub/sysreqs')"
-          sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))")
-          sudo -s eval "$sysreqs"
-
-      - name: Install dependencies
-        run: |
-          remotes::install_deps(dependencies = TRUE)
-          remotes::install_cran("rcmdcheck")
-        shell: Rscript {0}
-
-      - name: Session info
+          tar -xf data-raw/AMR_latest.tar.gz
+          Rscript -e "source('data-raw/_install_deps.R')"
+        shell: bash
+        
+      - name: Show session info
+        if: always()
        run: |
          options(width = 100)
-          pkgs <- installed.packages()[, "Package"]
-          sessioninfo::session_info(pkgs, include_base = TRUE)
+          utils::sessionInfo()
+          as.data.frame(utils::installed.packages())[, "Version", drop = FALSE]
        shell: Rscript {0}
-
-      - name: Check
+        
+      # - name: Only keep vignettes on release version
+      - name: Remove vignettes
+        # if: matrix.config.r != 'release'
+        if: always()
+        # writing to DESCRIPTION2 and then moving to DESCRIPTION is required for R < 3.3 as writeLines() cannot overwrite
+        run: |
+          rm -rf AMR/vignettes
+          Rscript -e "writeLines(readLines('AMR/DESCRIPTION')[!grepl('VignetteBuilder', readLines('AMR/DESCRIPTION'))], 'AMR/DESCRIPTION2')"
+          rm AMR/DESCRIPTION
+          mv AMR/DESCRIPTION2 AMR/DESCRIPTION
+        shell: bash
+        
+      - name: Run R CMD check
+        if: always()
        env:
          _R_CHECK_CRAN_INCOMING_: false
-        run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check")
-        shell: Rscript {0}
-
-      - name: Show testthat output
-        if: always()
-        run: find check -name 'testthat.Rout*' -exec cat '{}' \; || true
+          _R_CHECK_FORCE_SUGGESTS_: false
+          _R_CHECK_DEPENDS_ONLY_: true
+          _R_CHECK_LENGTH_1_CONDITION_: verbose
+          _R_CHECK_LENGTH_1_LOGIC2_: verbose
+          # during 'R CMD check', R_LIBS_USER will be overwritten, so:
+          R_LIBS_USER_GH_ACTIONS: ${{ env.R_LIBS_USER }}
+          R_RUN_TINYTEST: true
+        run: |
+          R CMD check --no-manual --run-donttest --run-dontrun AMR
        shell: bash

-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - name: Show unit tests output
+        if: always()
+        run: |
+          find . -name 'tinytest.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload artifacts
+        if: always()
+        uses: actions/upload-artifact@v2
        with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
+          name: artifacts-${{ matrix.config.os }}-r${{ matrix.config.r }}
+          path: AMR.Rcheck
--- a/.github/workflows/codecovr.yaml
+++ b/.github/workflows/codecovr.yaml
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,52 +20,76 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 on:
  push:
    branches:
+      - premaster
      - master
  pull_request:
    branches:
      - master

-name: code-tested
+name: code-coverage

 jobs:
-  code-tested:
+  code-coverage:
    runs-on: macOS-latest
    env:
      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
    steps:
      - uses: actions/checkout@v2

-      - uses: r-lib/actions/setup-r@master
-
+      - uses: r-lib/actions/setup-r@v1
+        with:
+          r-version: release
+          
      - uses: r-lib/actions/setup-pandoc@master

-      - name: Query dependencies
-        run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
-        shell: Rscript {0}
-
-      - name: Cache R packages
+      - name: Restore cached R packages
+        # this step will add the step 'Post Restore cached R packages' on a succesful run
        uses: actions/cache@v1
        with:
          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
+          key: macOS-latest-r-release-v4

-      - name: Install dependencies
+      - name: Unpack AMR and install R dependencies
        run: |
-          install.packages(c("remotes"))
-          remotes::install_deps(dependencies = TRUE)
-          remotes::install_cran("covr")
+          tar -xf data-raw/AMR_latest.tar.gz
+          Rscript -e "source('data-raw/_install_deps.R')"
+        shell: bash
+        
+      - name: Show session info
+        run: |
+          options(width = 100)
+          utils::sessionInfo()
+          as.data.frame(utils::installed.packages())[, "Version", drop = FALSE]
        shell: Rscript {0}

+      # - name: Test coverage
+      #   env:
+      #     CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+      #   run: |
+      #     library(AMR)
+      #     library(tinytest)
+      #     library(covr)
+      #     source_files <- list.files("R", pattern = ".R$", full.names = TRUE)
+      #     test_files <- list.files("inst/tinytest", full.names = TRUE)
+      #     cov <- file_coverage(source_files = source_files, test_files = test_files, parent_env = asNamespace("AMR"), line_exclusions = list("R/atc_online.R", "R/mo_source.R", "R/translate.R", "R/resistance_predict.R", "R/aa_helper_functions.R", "R/aa_helper_pm_functions.R", "R/zzz.R"))
+      #     attr(cov, which = "package") <- list(path = ".") # until https://github.com/r-lib/covr/issues/478 is solved
+      #     codecov(coverage = cov, quiet = FALSE)
+      #   shell: Rscript {0}
+      
      - name: Test coverage
-        run: covr::codecov(line_exclusions = list("R/atc_online.R", "R/mo_source.R", "R/resistance_predict.R", "R/aa_helper_functions.R", "R/aa_helper_pm_functions.R", "R/zzz.R"), quiet = FALSE)
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+          R_RUN_TINYTEST: true
+        run: |
+          library(AMR)
+          library(tinytest)
+          covr::codecov(line_exclusions = list("R/atc_online.R", "R/mo_source.R", "R/translate.R", "R/resistance_predict.R", "R/aa_helper_functions.R", "R/aa_helper_pm_functions.R", "R/zzz.R"))
        shell: Rscript {0}
+      
+      
--- a/.github/workflows/lintr.yaml
+++ b/.github/workflows/lintr.yaml
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 on:
@@ -66,5 +66,5 @@ jobs:
        shell: Rscript {0}

      - name: Lint
-        run: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_usage_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
+        run: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
        shell: Rscript {0}
--- a/22
+++ b/22
@@ -1,7 +1,7 @@
 Package: AMR
-Version: 1.4.0
-Date: 2020-10-08
-Title: Antimicrobial Resistance Analysis
+Version: 1.7.0
+Date: 2021-05-24
+Title: Antimicrobial Resistance Data Analysis
 Authors@R: c(
    person(role = c("aut", "cre"), 
           family = "Berends", given = c("Matthijs", "S."), email = "m.s.berends@umcg.nl", comment = c(ORCID = "0000-0001-7620-1800")),
@@ -29,31 +29,35 @@ Authors@R: c(
           family = "Meijer", given = c("Bart", "C."), email = "b.meijerg@certe.nl"),
    person(role = "ctb",
           family = "Ny", given = "Sofia", email = "sofia.ny@folkhalsomyndigheten.se"),
+    person(role = "ctb",
+           family = "Schade", given = c("Rogier", "P."), email = "r.schade@amsterdamumc.nl"),
    person(role = "ctb",
           family = "Souverein", given = "Dennis", email = "d.souvereing@streeklabhaarlem.nl"),
    person(role = "ctb",
           family = "Underwood", given = "Anthony", email = "au3@sanger.ac.uk"))
-Description: Functions to simplify the analysis and prediction of Antimicrobial 
-    Resistance (AMR) and to work with microbial and antimicrobial properties by
-    using evidence-based methods, like those defined by Leclercq et al. (2013) 
-    <doi:10.1111/j.1469-0691.2011.03703.x> and the Clinical and Laboratory 
-    Standards Institute (2014) <isbn: 1-56238-899-1>.
+Description: Functions to simplify and standardise antimicrobial resistance (AMR)
+    data analysis and to work with microbial and antimicrobial properties by
+    using evidence-based methods and reliable reference data such as LPSN
+    <doi:10.1099/ijsem.0.004332>.
 Depends:
    R (>= 3.0.0)
 Suggests:
    cleaner,
+    covr,
    curl,
    dplyr,
    ggplot2,
+    ggtext,
    knitr,
    microbenchmark,
+    pillar,
    readxl,
    rmarkdown,
    rstudioapi,
    rvest,
    skimr,
-    testthat,
    tidyr,
+    tinytest,
    xml2
 VignetteBuilder: knitr,rmarkdown
 URL: https://msberends.github.io/AMR/, https://github.com/msberends/AMR
--- a/113
+++ b/113
@@ -1,5 +1,21 @@
 # Generated by roxygen2: do not edit by hand

+S3method("!",mic)
+S3method("!=",ab_selector)
+S3method("!=",mic)
+S3method("%%",mic)
+S3method("%/%",mic)
+S3method("&",mic)
+S3method("*",mic)
+S3method("+",mic)
+S3method("-",mic)
+S3method("/",mic)
+S3method("<",mic)
+S3method("<=",mic)
+S3method("==",ab_selector)
+S3method("==",mic)
+S3method(">",mic)
+S3method(">=",mic)
 S3method("[",ab)
 S3method("[",disk)
 S3method("[",mic)
@@ -18,46 +34,114 @@ S3method("[[<-",disk)
 S3method("[[<-",mic)
 S3method("[[<-",mo)
 S3method("[[<-",rsi)
+S3method("^",mic)
+S3method("|",mic)
+S3method(abs,mic)
+S3method(acos,mic)
+S3method(acosh,mic)
+S3method(all,ab_selector)
+S3method(all,ab_selector_any_all)
+S3method(all,mic)
+S3method(any,ab_selector)
+S3method(any,ab_selector_any_all)
+S3method(any,mic)
 S3method(as.data.frame,ab)
 S3method(as.data.frame,mo)
 S3method(as.double,mic)
 S3method(as.integer,mic)
+S3method(as.list,custom_eucast_rules)
+S3method(as.list,custom_mdro_guideline)
+S3method(as.matrix,mic)
 S3method(as.numeric,mic)
 S3method(as.rsi,data.frame)
 S3method(as.rsi,default)
 S3method(as.rsi,disk)
 S3method(as.rsi,mic)
+S3method(asin,mic)
+S3method(asinh,mic)
+S3method(atan,mic)
+S3method(atanh,mic)
+S3method(barplot,disk)
 S3method(barplot,mic)
 S3method(barplot,rsi)
 S3method(c,ab)
+S3method(c,ab_selector)
+S3method(c,custom_eucast_rules)
+S3method(c,custom_mdro_guideline)
 S3method(c,disk)
 S3method(c,mic)
 S3method(c,mo)
 S3method(c,rsi)
+S3method(ceiling,mic)
+S3method(cos,mic)
+S3method(cosh,mic)
+S3method(cospi,mic)
+S3method(cummax,mic)
+S3method(cummin,mic)
+S3method(cumprod,mic)
+S3method(cumsum,mic)
+S3method(digamma,mic)
 S3method(droplevels,mic)
 S3method(droplevels,rsi)
+S3method(exp,mic)
+S3method(expm1,mic)
+S3method(floor,mic)
 S3method(format,bug_drug_combinations)
+S3method(gamma,mic)
+S3method(hist,mic)
 S3method(kurtosis,data.frame)
 S3method(kurtosis,default)
 S3method(kurtosis,matrix)
+S3method(lgamma,mic)
+S3method(log,mic)
+S3method(log10,mic)
+S3method(log1p,mic)
+S3method(log2,mic)
+S3method(max,mic)
+S3method(mean,mic)
+S3method(median,mic)
+S3method(min,mic)
+S3method(plot,disk)
 S3method(plot,mic)
 S3method(plot,resistance_predict)
 S3method(plot,rsi)
 S3method(print,ab)
 S3method(print,bug_drug_combinations)
 S3method(print,catalogue_of_life_version)
+S3method(print,custom_eucast_rules)
+S3method(print,custom_mdro_guideline)
 S3method(print,disk)
 S3method(print,mic)
 S3method(print,mo)
 S3method(print,mo_renamed)
 S3method(print,mo_uncertainties)
+S3method(print,pca)
 S3method(print,rsi)
+S3method(prod,mic)
+S3method(quantile,mic)
+S3method(range,mic)
+S3method(rep,mo)
+S3method(round,mic)
+S3method(sign,mic)
+S3method(signif,mic)
+S3method(sin,mic)
+S3method(sinh,mic)
+S3method(sinpi,mic)
 S3method(skewness,data.frame)
 S3method(skewness,default)
 S3method(skewness,matrix)
+S3method(sort,mic)
+S3method(sqrt,mic)
+S3method(sum,mic)
 S3method(summary,mic)
 S3method(summary,mo)
+S3method(summary,pca)
 S3method(summary,rsi)
+S3method(tan,mic)
+S3method(tanh,mic)
+S3method(tanpi,mic)
+S3method(trigamma,mic)
+S3method(trunc,mic)
 S3method(unique,ab)
 S3method(unique,disk)
 S3method(unique,mic)
@@ -65,6 +149,8 @@ S3method(unique,mo)
 S3method(unique,rsi)
 export("%like%")
 export("%like_case%")
+export("%unlike%")
+export("%unlike_case%")
 export(ab_atc)
 export(ab_atc_group1)
 export(ab_atc_group2)
@@ -82,8 +168,10 @@ export(ab_tradenames)
 export(ab_url)
 export(age)
 export(age_groups)
+export(all_antimicrobials)
 export(aminoglycosides)
 export(anti_join_microorganisms)
+export(antimicrobials_equal)
 export(as.ab)
 export(as.disk)
 export(as.mic)
@@ -93,6 +181,7 @@ export(atc_online_ddd)
 export(atc_online_groups)
 export(atc_online_property)
 export(availability)
+export(betalactams)
 export(brmo)
 export(bug_drug_combinations)
 export(carbapenems)
@@ -112,6 +201,9 @@ export(count_all)
 export(count_df)
 export(count_resistant)
 export(count_susceptible)
+export(custom_eucast_rules)
+export(custom_mdro_guideline)
+export(eucast_dosage)
 export(eucast_exceptional_phenotypes)
 export(eucast_rules)
 export(facet_rsi)
@@ -122,6 +214,7 @@ export(filter_4th_cephalosporins)
 export(filter_5th_cephalosporins)
 export(filter_ab_class)
 export(filter_aminoglycosides)
+export(filter_betalactams)
 export(filter_carbapenems)
 export(filter_cephalosporins)
 export(filter_first_isolate)
@@ -129,6 +222,7 @@ export(filter_first_weighted_isolate)
 export(filter_fluoroquinolones)
 export(filter_glycopeptides)
 export(filter_macrolides)
+export(filter_oxazolidinones)
 export(filter_penicillins)
 export(filter_tetracyclines)
 export(first_isolate)
@@ -136,6 +230,7 @@ export(fluoroquinolones)
 export(full_join_microorganisms)
 export(g.test)
 export(geom_rsi)
+export(get_episode)
 export(get_locale)
 export(get_mo_source)
 export(ggplot_pca)
@@ -150,8 +245,12 @@ export(is.mic)
 export(is.mo)
 export(is.rsi)
 export(is.rsi.eligible)
+export(is_new_episode)
+export(italicise_taxonomy)
+export(italicize_taxonomy)
 export(key_antibiotics)
 export(key_antibiotics_equal)
+export(key_antimicrobials)
 export(kurtosis)
 export(labels_rsi_count)
 export(left_join_microorganisms)
@@ -169,6 +268,10 @@ export(mo_fullname)
 export(mo_genus)
 export(mo_gramstain)
 export(mo_info)
+export(mo_is_gram_negative)
+export(mo_is_gram_positive)
+export(mo_is_intrinsic_resistant)
+export(mo_is_yeast)
 export(mo_kingdom)
 export(mo_matching_score)
 export(mo_name)
@@ -190,6 +293,7 @@ export(mo_url)
 export(mo_year)
 export(mrgn)
 export(n_rsi)
+export(oxazolidinones)
 export(p_symbol)
 export(pca)
 export(penicillins)
@@ -199,6 +303,9 @@ export(proportion_R)
 export(proportion_S)
 export(proportion_SI)
 export(proportion_df)
+export(random_disk)
+export(random_mic)
+export(random_rsi)
 export(resistance)
 export(resistance_predict)
 export(right_join_microorganisms)
@@ -215,15 +322,19 @@ export(theme_rsi)
 importFrom(graphics,arrows)
 importFrom(graphics,axis)
 importFrom(graphics,barplot)
-importFrom(graphics,par)
+importFrom(graphics,hist)
+importFrom(graphics,legend)
+importFrom(graphics,mtext)
 importFrom(graphics,plot)
 importFrom(graphics,points)
 importFrom(graphics,text)
 importFrom(stats,complete.cases)
 importFrom(stats,glm)
 importFrom(stats,lm)
+importFrom(stats,median)
 importFrom(stats,pchisq)
 importFrom(stats,prcomp)
 importFrom(stats,predict)
 importFrom(stats,qchisq)
+importFrom(stats,quantile)
 importFrom(stats,var)
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,14 +1,255 @@
-# AMR 1.4.0
+# `AMR` 1.7.0


-Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt about this package to. We are those reviewers very grateful for going through our code so thoroughly!
+### Breaking change
+* All antibiotic class selectors (such as `carbapenems()`, `aminoglycosides()`) can now be used for filtering as well, making all their accompanying `filter_*()` functions redundant (such as `filter_carbapenems()`, `filter_aminoglycosides()`). These functions are now deprecated and will be removed in a next release.
+  ```r
+  # select columns with results for carbapenems
+  example_isolates[, carbapenems()]           # base R
+  example_isolates %>% select(carbapenems())  # dplyr
+  
+  # filter rows for resistance in any carbapenem
+  example_isolates[any(carbapenems() == "R"), ]                  # base R
+  example_isolates %>% filter(any(carbapenems() == "R"))         # dplyr
+  example_isolates %>% filter(if_any(carbapenems(), ~.x == "R")) # dplyr (formal)
+  
+  # filter rows for resistance in all carbapenems
+  example_isolates[all(carbapenems() == "R"), ]           # base R
+  example_isolates[carbapenems() == "R", ]
+  example_isolates %>% filter(all(carbapenems() == "R"))  # dplyr
+  example_isolates %>% filter(carbapenems() == "R")
+  ```

 ### New
-* Support for 'EUCAST Expert Rules' / 'EUCAST Intrinsic Resistance and Unusual Phenotypes' version 3.2 of May 2020. With this addition to the previously implemented version 3.1 of 2016, the `eucast_rules()` function can now correct for more than 180 different antibiotics and the `mdro()` function can determine multidrug resistance based on more than 150 different antibiotics. All previously implemented versions of the EUCAST rules are now maintained and kept available in this package. The `eucast_rules()` function consequently gained the parameters `version_breakpoints` (at the moment defaults to v10.0, 2020) and `version_expertrules` (at the moment defaults to v3.2, 2020). The `example_isolates` data set now also reflects the change from v3.1 to v3.2. The `mdro()` function now accepts `guideline == "EUCAST3.1"` and `guideline == "EUCAST3.2"`.
+* Function `custom_eucast_rules()` that brings support for custom AMR rules in `eucast_rules()`
+* Function `italicise_taxonomy()` to make taxonomic names within a string italic, with support for markdown and ANSI
+* Support for all four methods to determine first isolates as summarised by Hindler *et al.* (doi: [10.1086/511864](https://doi.org/10.1086/511864)): isolate-based, patient-based, episode-based and phenotype-based. The last method is now the default.
+  * The `first_isolate()` function gained the argument `method` that has to be "phenotype-based", "episode-based", "patient-based", or "isolate-based". The old behaviour is equal to "episode-based". The new default is "phenotype-based" if antimicrobial test results are available, and "episode-based" otherwise. This new default will yield slightly more isolates for selection (which is a good thing).
+  * Since fungal isolates can also be selected, the functions `key_antibiotics()` and `key_antibiotics_equal()` are now deprecated in favour of the `key_antimicrobials()` and `antimicrobials_equal()` functions. Also, the new `all_antimicrobials()` function works like the old `key_antibiotics()` function, but includes any column with antimicrobial test results. Using `key_antimicrobials()` still only selects six preferred antibiotics for Gram-negatives, six for Gram-positives, and six universal antibiotics. It has a new `antifungal` argument to set antifungal agents (antimycotics).
+  * Using `type == "points"` in the `first_isolate()` function for phenotype-based selection will now consider all antimicrobial drugs in the data set, using the new `all_antimicrobials()`
+  * The `first_isolate()` function can now take a vector of values for `col_keyantibiotics` and can have an episode length of `Inf`
+  * Since the phenotype-based method is the new default, `filter_first_isolate()` renders the `filter_first_weighted_isolate()` function redundant. For this reason, `filter_first_weighted_isolate()` is now deprecated.
+  * The documentation of the `first_isolate()` and `key_antimicrobials()` functions has been completely rewritten.
+* Function `betalactams()` as additional antbiotic column selector and function `filter_betalactams()` as additional antbiotic column filter. The group of betalactams consists of all carbapenems, cephalosporins and penicillins.
+* A `ggplot()` method for `resistance_predict()` 
+
+
+### Changed
+* Custom MDRO guidelines (`mdro()`, `custom_mdro_guideline()`):
+  * Custom MDRO guidelines can now be combined with other custom MDRO guidelines using `c()`
+  * Fix for applying the rules; in previous versions, rows were interpreted according to the last matched rule. Now, rows are interpreted according to the first matched rule
+* Fix for `age_groups()` for persons aged zero
+* The `example_isolates` data set now contains some (fictitious) zero-year old patients
+* Fix for minor translation errors
+* Printing of microbial codes in a `data.frame` or `tibble` now gives a warning if the data contains old microbial codes (from a previous AMR package version)
+* Extended the `like()` functions:
+  * Now checks if `pattern` is a *valid* regular expression
+  * Added `%unlike%` and `%unlike_case%` (as negations of the existing `%like%` and `%like_case%`). This greatly improves readability:
+    ```r
+    if (!grepl("EUCAST", guideline)) ...
+    # same:
+    if (guideline %unlike% "EUCAST") ...
+    ```
+  * Altered the RStudio addin, so it now iterates over `%like%` -> `%unlike%` -> `%like_case%` -> `%unlike_case%` if you keep pressing your keyboard shortcut
+* Fixed an installation error on R-3.0
+* Added `info` argument to `as.mo()` to turn on/off the progress bar
+* Fixed a bug where `col_mo` in some functions (esp. `eucast_rules()` and `mdro()`) could not be a column name of the `microorganisms` data set as it would throw an error
+* Fix for transforming numeric values to RSI (`as.rsi()`) when the `vctrs` package is loaded (i.e., when using tidyverse)
+* Colour fix for using `barplot()` on an RSI class
+* Added 25 common system codes for bacteria to the `microorganisms.codes` data set
+* Added 16 common system codes for antimicrobial agents to the `antibiotics` data set
+* Fix for using `skimr::skim()` on classes `mo`, `mic` and `disk` when using the just released `dplyr` v1.0.6
+* Updated `skimr::skim()` usage for MIC values to also include 25th and 75th percentiles
+* Fix for plotting missing MIC/disk diffusion values
+* Updated join functions to always use `dplyr` join functions if the `dplyr` package is installed - now also preserving grouped variables
+* Antibiotic class selectors (such as `cephalosporins()`) now maintain the column order from the original data
+* Fix for selecting columns using `fluoroquinolones()`
+* `age()` now vectorises over both `x` and `reference`
+
+### Other
+* All unit tests are now processed by the `tinytest` package, instead of the `testthat` package. The `testthat` package unfortunately requires tons of dependencies that are also heavy and only usable for recent R versions, disallowing developers to test a package under any R 3.* version. On the contrary, the `tinytest` package is very lightweight and dependency-free.
+
+
+# `AMR` 1.6.0
+
+### New
+* Support for EUCAST Clinical Breakpoints v11.0 (2021), effective in the `eucast_rules()` function and in `as.rsi()` to interpret MIC and disk diffusion values. This is now the default guideline in this package.
+  * Added function `eucast_dosage()` to get a `data.frame` with advised dosages of a certain bug-drug combination, which is based on the new `dosage` data set
+  * Added data set `dosage` to fuel the new `eucast_dosage()` function and to make this data available in a structured way 
+  * Existing data set `example_isolates` now reflects the latest EUCAST rules
+* Added argument `only_rsi_columns` for some functions, which defaults to `FALSE`, to indicate if the functions must only be applied to columns that are of class `<rsi>` (i.e., transformed with `as.rsi()`). This increases speed since automatic determination of antibiotic columns is not needed anymore. Affected functions are:
+  * All antibiotic selector functions (`ab_class()` and its wrappers, such as `aminoglycosides()`, `carbapenems()`, `penicillins()`)
+  * All antibiotic filter functions (`filter_ab_class()` and its wrappers, such as `filter_aminoglycosides()`, `filter_carbapenems()`, `filter_penicillins()`)
+  * `eucast_rules()`
+  * `mdro()` (including wrappers such as `brmo()`, `mrgn()` and `eucast_exceptional_phenotypes()`)
+  * `guess_ab_col()`
+* Functions `oxazolidinones()` (an antibiotic selector function) and `filter_oxazolidinones()` (an antibiotic filter function) to select/filter on e.g. linezolid and tedizolid
+  ```r
+  library(dplyr)
+  x <- example_isolates %>% select(date, hospital_id, oxazolidinones())
+  #> Selecting oxazolidinones: column 'LNZ' (linezolid)
+  
+  x <- example_isolates %>% filter_oxazolidinones()
+  #> Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either "R", "S" or "I"
+   ```
+* Support for custom MDRO guidelines, using the new `custom_mdro_guideline()` function, please see `mdro()` for additional info
+* `ggplot()` generics for classes `<mic>` and `<disk>`
+* Function `mo_is_yeast()`, which determines whether a microorganism is a member of the taxonomic class Saccharomycetes or the taxonomic order Saccharomycetales:
+  ```r
+  mo_kingdom(c("Aspergillus", "Candida"))
+  #> [1] "Fungi" "Fungi"
+  
+  mo_is_yeast(c("Aspergillus", "Candida"))
+  #> [1] FALSE  TRUE
+  
+  # usage for filtering data:
+  example_isolates[which(mo_is_yeast()), ]   # base R
+  example_isolates %>% filter(mo_is_yeast()) # dplyr
+  ```
+  The `mo_type()` function has also been updated to reflect this change:
+  ```r
+  mo_type(c("Aspergillus", "Candida"))
+  # [1] "Fungi"  "Yeasts"
+  mo_type(c("Aspergillus", "Candida"), language = "es") # also supported: de, nl, fr, it, pt
+  #> [1] "Hongos"    "Levaduras"
+  ```
+* Added Pretomanid (PMD, J04AK08) to the `antibiotics` data set
+* MIC values (see `as.mic()`) can now be used in any mathematical processing, such as usage inside functions `min()`, `max()`, `range()`, and with binary operators (`+`, `-`, etc.). This allows for easy distribution analysis and fast filtering on MIC values:
+  ```r
+  x <- random_mic(10)
+  x
+  #> Class <mic>
+  #>  [1] 128   0.5   2     0.125 64    0.25  >=256 8     16    4
+  x[x > 4]
+  #> Class <mic>
+  #> [1] 128   64    >=256 8     16
+  range(x)
+  #> [1]   0.125 256.000
+  range(log2(x))
+  #> [1] -3  8
+  ```
+
+### Changed
+* Updated the bacterial taxonomy to 3 March 2021 (using [LPSN](https://lpsn.dsmz.de))
+  * Added 3,372 new species and 1,523 existing species became synomyms
+  * The URL of a bacterial species (`mo_url()`) will now lead to https://lpsn.dsmz.de
+* Big update for plotting classes `rsi`, `<mic>`, and `<disk>`:
+  * Plotting of MIC and disk diffusion values now support interpretation colouring if you supply the microorganism and antimicrobial agent
+  * All colours were updated to colour-blind friendly versions for values R, S and I for all plot methods (also applies to tibble printing)
+  * Interpretation of MIC and disk diffusion values to R/SI will now be translated if the system language is German, Dutch or Spanish (see `translate`)
+  * Plotting is now possible with base R using `plot()` and with ggplot2 using `ggplot()` on any vector of MIC and disk diffusion values
+* Updated SNOMED codes to US Edition of SNOMED CT from 1 September 2020 and added the source to the help page of the `microorganisms` data set
+* `is.rsi()` and `is.rsi.eligible()` now return a vector of `TRUE`/`FALSE` when the input is a data set, by iterating over all columns
+* Using functions without setting a data set (e.g., `mo_is_gram_negative()`, `mo_is_gram_positive()`, `mo_is_intrinsic_resistant()`, `first_isolate()`, `mdro()`) now work with `dplyr`s `group_by()` again
+* `first_isolate()` can be used with `group_by()` (also when using a dot `.` as input for the data) and now returns the names of the groups
+* Updated the data set `microorganisms.codes` (which contains popular LIS and WHONET codes for microorganisms) for some species of *Mycobacterium* that previously incorrectly returned *M. africanum*
+* WHONET code `"PNV"` will now correctly be interpreted as `PHN`, the antibiotic code for phenoxymethylpenicillin ('peni V')
+* Fix for verbose output of `mdro(..., verbose = TRUE)` for German guideline (3MGRN and 4MGRN) and Dutch guideline (BRMO, only *P. aeruginosa*)
+* `is.rsi.eligible()` now detects if the column name resembles an antibiotic name or code and now returns `TRUE` immediately if the input contains any of the values "R", "S" or "I". This drastically improves speed, also for a lot of other functions that rely on automatic determination of antibiotic columns.
+* Functions `get_episode()` and `is_new_episode()` now support less than a day as value for argument `episode_days` (e.g., to include one patient/test per hour)
+* Argument `ampc_cephalosporin_resistance` in `eucast_rules()` now also applies to value "I" (not only "S")
+* Functions `print()` and `summary()` on a Principal Components Analysis object (`pca()`) now print additional group info if the original data was grouped using `dplyr::group_by()`
+* Improved speed and reliability of `guess_ab_col()`. As this also internally improves the reliability of `first_isolate()` and `mdro()`, this might have a slight impact on the results of those functions.
+* Fix for `mo_name()` when used in other languages than English
+* The `like()` function (and its fast alias `%like%`) now always use Perl compatibility, improving speed for many functions in this package (e.g., `as.mo()` is now up to 4 times faster)
+* *Staphylococcus cornubiensis* is now correctly categorised as coagulase-positive
+* `random_disk()` and `random_mic()` now have an expanded range in their randomisation
+* Support for GISA (glycopeptide-intermediate *S. aureus*), so e.g. `mo_genus("GISA")` will return `"Staphylococcus"` 
+* Added translations of German and Spanish for more than 200 antimicrobial drugs
+* Speed improvement for `as.ab()` when the input is an official name or ATC code
+* Added argument `include_untested_rsi` to the `first_isolate()` functions (defaults to `TRUE` to keep existing behaviour), to be able to exclude rows where all R/SI values (class `<rsi>`, see `as.rsi()`) are empty
+
+### Other
+* Big documentation updates
+* Loading the package (i.e., `library(AMR)`) now is ~50 times faster than before, in costs of package size (which increased by ~3 MB)
+
+
+# `AMR` 1.5.0
+
+### New
+* Functions `get_episode()` and `is_new_episode()` to determine (patient) episodes which are not necessarily based on microorganisms. The `get_episode()` function returns the index number of the episode per group, while the `is_new_episode()` function returns values `TRUE`/`FALSE` to indicate whether an item in a vector is the start of a new episode. They also support `dplyr`s grouping (i.e. using `group_by()`):
+  ```r
+  library(dplyr)
+  example_isolates %>%
+    group_by(patient_id, hospital_id) %>%
+    filter(is_new_episode(date, episode_days = 60))
+  ```
+* Functions `mo_is_gram_negative()` and `mo_is_gram_positive()` as wrappers around `mo_gramstain()`. They always return `TRUE` or `FALSE` (except when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
+* Function `mo_is_intrinsic_resistant()` to test for intrinsic resistance, based on [EUCAST Intrinsic Resistance and Unusual Phenotypes v3.2](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) from 2020.
+* Functions `random_mic()`, `random_disk()` and `random_rsi()` for random value generation. The functions `random_mic()` and `random_disk()` take microorganism names and antibiotic names as input to make generation more realistic.
+
+### Changed
+* New argument `ampc_cephalosporin_resistance` in `eucast_rules()` to correct for AmpC de-repressed cephalosporin-resistant mutants
+* Interpretation of antimicrobial resistance - `as.rsi()`:
+  * Reference data used for `as.rsi()` can now be set by the user, using the `reference_data` argument. This allows for using own interpretation guidelines. The user-set data must have the same structure as `rsi_translation`.
+  * Better determination of disk zones and MIC values when running `as.rsi()` on a data.frame
+  * Fix for using `as.rsi()` on a data.frame in older R versions
+  * `as.rsi()` on a data.frame will not print a message anymore if the values are already clean R/SI values
+  * If using `as.rsi()` on MICs or disk diffusion while there is intrinsic antimicrobial resistance, a warning will be thrown to remind about this
+  * Fix for using `as.rsi()` on a `data.frame` that only contains one column for antibiotic interpretations
+* Some functions are now context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the data argument does not need to be set anymore. This is the case for the new functions:
+  * `mo_is_gram_negative()`
+  * `mo_is_gram_positive()`
+  * `mo_is_intrinsic_resistant()`
+  
+  ... and for the existing functions:
+  * `first_isolate()`,
+  * `key_antibiotics()`,
+  * `mdro()`,
+  * `brmo()`,
+  * `mrgn()`,
+  * `mdr_tb()`,
+  * `mdr_cmi2012()`,
+  * `eucast_exceptional_phenotypes()`
+  
+  ```r
+  # to select first isolates that are Gram-negative 
+  # and view results of cephalosporins and aminoglycosides:
+  library(dplyr)
+  example_isolates %>%
+    filter(first_isolate(), mo_is_gram_negative()) %>% 
+    select(mo, cephalosporins(), aminoglycosides()) %>% 
+    as_tibble()
+  ```
+* For antibiotic selection functions (such as `cephalosporins()`, `aminoglycosides()`) to select columns based on a certain antibiotic group, the dependency on the `tidyselect` package was removed, meaning that they can now also be used without the need to have this package installed and now also work in base R function calls (they rely on R 3.2 or later):
+  ```r
+  # above example in base R:
+  example_isolates[which(first_isolate() & mo_is_gram_negative()),
+                   c("mo", cephalosporins(), aminoglycosides())]
+  ```
+* For all function arguments in the code, it is now defined what the exact type of user input should be (inspired by the [`typed`](https://github.com/moodymudskipper/typed) package). If the user input for a certain function does not meet the requirements for a specific argument (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 420 arguments were defined.
+* Fix for `set_mo_source()`, that previously would not remember the file location of the original file
+* Deprecated function `p_symbol()` that not really fits the scope of this package. It will be removed in a future version. See [here](https://github.com/msberends/AMR/blob/v1.4.0/R/p_symbol.R) for the source code to preserve it.
+* Updated coagulase-negative staphylococci determination with Becker *et al.* 2020 (PMID 32056452), meaning that the species *S. argensis*, *S. caeli*, *S. debuckii*, *S. edaphicus* and *S. pseudoxylosus* are now all considered CoNS
+* Fix for using argument `reference_df` in `as.mo()` and `mo_*()` functions that contain old microbial codes (from previous package versions)
+* Fixed a bug where `mo_uncertainties()` would not return the results based on the MO matching score
+* Fixed a bug where `as.mo()` would not return results for known laboratory codes for microorganisms
+* Fixed a bug where `as.ab()` would sometimes fail
+* Better tibble printing for MIC values
+* Fix for plotting MIC values with `plot()`
+* Added `plot()` generic to class `<disk>`
+* LA-MRSA and CA-MRSA are now recognised as an abbreviation for *Staphylococcus aureus*, meaning that e.g. `mo_genus("LA-MRSA")` will return `"Staphylococcus"` and `mo_is_gram_positive("LA-MRSA")` will return `TRUE`.
+* Fix for printing class <mo> in tibbles when all values are `NA`
+* Fix for `mo_shortname()` when the input contains `NA`
+* If `as.mo()` takes more than 30 seconds, some suggestions will be done to improve speed
+
+### Other
+* All messages and warnings thrown by this package now break sentences on whole words
+* More extensive unit tests
+* Internal calls to `options()` were all removed in favour of a new internal environment `pkg_env`
+* Improved internal type setting (among other things: replaced all `sapply()` calls with `vapply()`)
+* Added CodeFactor as a continuous code review to this package: <https://www.codefactor.io/repository/github/msberends/amr/>
+* Added Dr. Rogier Schade as contributor
+
+# `AMR` 1.4.0
+
+### New
+* Support for 'EUCAST Expert Rules' / 'EUCAST Intrinsic Resistance and Unusual Phenotypes' version 3.2 of May 2020. With this addition to the previously implemented version 3.1 of 2016, the `eucast_rules()` function can now correct for more than 180 different antibiotics and the `mdro()` function can determine multidrug resistance based on more than 150 different antibiotics. All previously implemented versions of the EUCAST rules are now maintained and kept available in this package. The `eucast_rules()` function consequently gained the arguments `version_breakpoints` (at the moment defaults to v10.0, 2020) and `version_expertrules` (at the moment defaults to v3.2, 2020). The `example_isolates` data set now also reflects the change from v3.1 to v3.2. The `mdro()` function now accepts `guideline == "EUCAST3.1"` and `guideline == "EUCAST3.2"`.
 * A new vignette and website page with info about all our public and freely available data sets, that can be downloaded as flat files or in formats for use in R, SPSS, SAS, Stata and Excel: https://msberends.github.io/AMR/articles/datasets.html
 * Data set `intrinsic_resistant`. This data set contains all bug-drug combinations where the 'bug' is intrinsic resistant to the 'drug' according to the latest EUCAST insights. It contains just two columns: `microorganism` and `antibiotic`.

  Curious about which enterococci are actually intrinsic resistant to vancomycin?
+  
  ```r
  library(AMR)
  library(dplyr)
@@ -35,7 +276,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
    ```
  * Cleaning columns in a data.frame now allows you to specify those columns with tidy selection, e.g. `as.rsi(df, col1:col9)`
  * Big speed improvement for interpreting MIC values and disk zone diameters. When interpreting 5,000 MIC values of two antibiotics (10,000 values in total), our benchmarks showed a total run time going from 80.7-85.1 seconds to 1.8-2.0 seconds.
-  * Added parameter 'add_intrinsic_resistance' (defaults to `FALSE`), that considers intrinsic resistance according to EUCAST
+  * Added argument 'add_intrinsic_resistance' (defaults to `FALSE`), that considers intrinsic resistance according to EUCAST
  * Fixed a bug where in EUCAST rules the breakpoint for R would be interpreted as ">=" while this should have been "<"
 * Added intelligent data cleaning to `as.disk()`, so numbers can also be extracted from text and decimal numbers will always be rounded up:
  ```r
@@ -46,7 +287,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 * Improvements for `as.mo()`:
  * A completely new matching score for ambiguous user input, using `mo_matching_score()`. Any user input value that could mean more than one taxonomic entry is now considered 'uncertain'. Instead of a warning, a message will be thrown and the accompanying `mo_uncertainties()` has been changed completely; it now prints all possible candidates with their matching score.
  * Big speed improvement for already valid microorganism ID. This also means an significant speed improvement for using `mo_*` functions like `mo_name()` on microoganism IDs.
-  * Added parameter `ignore_pattern` to `as.mo()` which can also be given to `mo_*` functions like `mo_name()`, to exclude known non-relevant input from analysing. This can also be set with the option `AMR_ignore_pattern`.
+  * Added argument `ignore_pattern` to `as.mo()` which can also be given to `mo_*` functions like `mo_name()`, to exclude known non-relevant input from analysing. This can also be set with the option `AMR_ignore_pattern`.
 * `get_locale()` now uses at default `Sys.getenv("LANG")` or, if `LANG` is not set, `Sys.getlocale()`. This can be overwritten by setting the option `AMR_locale`.
 * Big speed improvement for `eucast_rules()`
 * Overall speed improvement by tweaking joining functions
@@ -57,7 +298,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 * Updated the documentation of the `WHONET` data set to clarify that all patient names are fictitious
 * Small `as.ab()` algorithm improvements
 * Fix for combining MIC values with raw numbers, i.e. `c(as.mic(2), 2)` previously failed but now returns a valid MIC class
-* `ggplot_rsi()` and `geom_rsi()` gained parameters `minimum` and `language`, to influence the internal use of `rsi_df()`
+* `ggplot_rsi()` and `geom_rsi()` gained arguments `minimum` and `language`, to influence the internal use of `rsi_df()`
 * Changes in the `antibiotics` data set:
  * Updated oral and parental DDDs from the WHOCC
  * Added abbreviation "piptazo" to 'Piperacillin/tazobactam' (TZP)
@@ -65,14 +306,14 @@ Note: some changes in this version were suggested by anonymous reviewers from th
  * 'Penicillin V' (for oral use, code `PNV`) was removed, since its actual entry 'Phenoxymethylpenicillin' (code `PHN`) already existed
  * The group name (`antibiotics$group`) of 'Linezolid' (`LNZ`), 'Cycloserine' (`CYC`), 'Tedizolid' (`TZD`) and 'Thiacetazone' (`THA`) is now "Oxazolidinones" instead of "Other antibacterials"
 * Added support for using `unique()` on classes `<rsi>`, `<mic>`, `<disk>`, `<ab>` and `<mo>`
-* Added parameter `excess` to the `kurtosis()` function (defaults to `FALSE`), to return the *excess kurtosis*, defined as the kurtosis minus three.
+* Added argument `excess` to the `kurtosis()` function (defaults to `FALSE`), to return the *excess kurtosis*, defined as the kurtosis minus three.

 ### Other
 * Removed functions `portion_R()`, `portion_S()` and `portion_I()` that were deprecated since version 0.9.0 (November 2019) and were replaced with `proportion_R()`, `proportion_S()` and `proportion_I()`
 * Removed unnecessary references to the `base` package
 * Added packages that could be useful for some functions to the `Suggests` field of the `DESCRIPTION` file

-# AMR 1.3.0
+# `AMR` 1.3.0

 ### New
 * Function `ab_from_text()` to retrieve antimicrobial drug names, doses and forms of administration from clinical texts in e.g. health care records, which also corrects for misspelling since it uses `as.ab()` internally
@@ -90,7 +331,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 * Added official antimicrobial names to all `filter_ab_class()` functions, such as `filter_aminoglycosides()`
 * Added antibiotics code "FOX1" for cefoxitin screening (abbreviation "cfsc") to the `antibiotics` data set
 * Added Monuril as trade name for fosfomycin
-* Added parameter `conserve_capped_values` to `as.rsi()` for interpreting MIC values - it makes sure that values starting with "<" (but not "<=") will always return "S" and values starting with ">" (but not ">=") will always return "R". The default behaviour of `as.rsi()` has not changed, so you need to specifically do `as.rsi(..., conserve_capped_values = TRUE)`.
+* Added argument `conserve_capped_values` to `as.rsi()` for interpreting MIC values - it makes sure that values starting with "<" (but not "<=") will always return "S" and values starting with ">" (but not ">=") will always return "R". The default behaviour of `as.rsi()` has not changed, so you need to specifically do `as.rsi(..., conserve_capped_values = TRUE)`.

 ### Changed
 * Big speed improvement for using any function on microorganism codes from earlier package versions (prior to `AMR` v1.2.0), such as `as.mo()`, `mo_name()`, `first_isolate()`, `eucast_rules()`, `mdro()`, etc.
@@ -125,7 +366,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 ### Other
 * Moved primary location of this project from GitLab to [GitHub](https://github.com/msberends/AMR), giving us native support for automated syntax checking without being dependent on external services such as AppVeyor and Travis CI.

-# AMR 1.2.0
+# `AMR` 1.2.0

 ### Breaking 
 * Removed code dependency on all other R packages, making this package fully independent of the development process of others. This is a major code change, but will probably not be noticeable by most users.
@@ -163,7 +404,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 * Removed previously deprecated function `p.symbol()` - it was replaced with `p_symbol()`
 * Removed function `read.4d()`, that was only useful for reading data from an old test database.

-# AMR 1.1.0
+# `AMR` 1.1.0

 ### New
 * Support for easy principal component analysis for AMR, using the new `pca()` function 
@@ -185,7 +426,7 @@ Note: some changes in this version were suggested by anonymous reviewers from th
 * Support for the upcoming `dplyr` version 1.0.0
 * More robust assigning for classes `rsi` and `mic`

-# AMR 1.0.1
+# `AMR` 1.0.1

 ### Changed
 * Fixed important floating point error for some MIC comparisons in EUCAST 2020 guideline
@@ -198,10 +439,10 @@ Note: some changes in this version were suggested by anonymous reviewers from th
    mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = .$mybacteria)
  ```
 * Added antibiotic abbreviations for a laboratory manufacturer (GLIMS) for cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin and trimethoprim/sulfamethoxazole
-* Added `uti` (as abbreviation of urinary tract infections) as parameter to `as.rsi()`, so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
+* Added `uti` (as abbreviation of urinary tract infections) as argument to `as.rsi()`, so interpretation of MIC values and disk zones can be made dependent on isolates specifically from UTIs
 * Info printing in functions `eucast_rules()`, `first_isolate()`, `mdro()` and `resistance_predict()` will now at default only print when R is in an interactive mode (i.e. not in RMarkdown)

-# AMR 1.0.0
+# `AMR` 1.0.0

 This software is now out of beta and considered stable. Nonetheless, this package will be developed continually.

@@ -237,7 +478,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
  * Speed improvements, especially for the *G. species* format (G for genus), like *E. coli* and *K pneumoniae*
  * Support for more common codes used in laboratory information systems
 * Input values for `as.disk()` limited to a maximum of 50 millimeters
-* Added a lifecycle state to every function, following [the lifecycle circle of the `tidyverse`](https://www.tidyverse.org/lifecycle)
+* Added a lifecycle state to every function, following the lifecycle circle of the `tidyverse`
 * For in `as.ab()`: support for drugs starting with "co-" like co-amoxiclav, co-trimoxazole, co-trimazine and co-trimazole (thanks to Peter Dutey)
 * Changes to the `antibiotics` data set (thanks to Peter Dutey):
  * Added more synonyms to colistin, imipenem and piperacillin/tazobactam
@@ -249,7 +490,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 * Full support for the upcoming R 4.0
 * Removed unnecessary `AMR::` calls

-# AMR 0.9.0
+# `AMR` 0.9.0

 ### Breaking
 * Adopted Adeolu *et al.* (2016), [PMID 27620848](https:/pubmed.ncbi.nlm.nih.gov/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.
@@ -315,10 +556,10 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 * Change dependency on `clean` to `cleaner`, as this package was renamed accordingly upon CRAN request
 * Added Dr. Sofia Ny as contributor

-# AMR 0.8.0
+# `AMR` 0.8.0

 ### Breaking
-* Determination of first isolates now **excludes** all 'unknown' microorganisms at default, i.e. microbial code `"UNKNOWN"`. They can be included with the new parameter `include_unknown`:
+* Determination of first isolates now **excludes** all 'unknown' microorganisms at default, i.e. microbial code `"UNKNOWN"`. They can be included with the new argument `include_unknown`:
  ```r
  first_isolate(..., include_unknown = TRUE)
  ```
@@ -369,7 +610,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
  ```r
  format(x, combine_IR = FALSE)
  ```
-* Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibility of a combination therapy. A new parameter `only_all_tested` (**which defaults to `FALSE`**) replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:
+* Additional way to calculate co-resistance, i.e. when using multiple antimicrobials as input for `portion_*` functions or `count_*` functions. This can be used to determine the empiric susceptibility of a combination therapy. A new argument `only_all_tested` (**which defaults to `FALSE`**) replaces the old `also_single_tested` and can be used to select one of the two methods to count isolates and calculate portions. The difference can be seen in this example table (which is also on the `portion` and `count` help pages), where the %SI is being determined:

  ```r
  # --------------------------------------------------------------------
@@ -425,13 +666,13 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 * Removed deprecated functions `abname()`, `ab_official()`, `atc_name()`, `atc_official()`, `atc_property()`, `atc_tradenames()`, `atc_trivial_nl()`
 * Fix and speed improvement for `mo_shortname()`
 * Fix for using `mo_*` functions where the coercion uncertainties and failures would not be available through `mo_uncertainties()` and `mo_failures()` anymore
-* Deprecated the `country` parameter of `mdro()` in favour of the already existing `guideline` parameter to support multiple guidelines within one country
+* Deprecated the `country` argument of `mdro()` in favour of the already existing `guideline` argument to support multiple guidelines within one country
 * The `name` of `RIF` is now Rifampicin instead of Rifampin
 * The `antibiotics` data set is now sorted by name and all cephalosporins now have their generation between brackets
 * Speed improvement for `guess_ab_col()` which is now 30 times faster for antibiotic abbreviations
 * Improved `filter_ab_class()` to be more reliable and to support 5th generation cephalosporins
 * Function `availability()` now uses `portion_R()` instead of `portion_IR()`, to comply with EUCAST insights
-* Functions `age()` and `age_groups()` now have a `na.rm` parameter to remove empty values
+* Functions `age()` and `age_groups()` now have a `na.rm` argument to remove empty values
 * Renamed function `p.symbol()` to `p_symbol()` (the former is now deprecated and will be removed in a future version)
 * Using negative values for `x` in `age_groups()` will now introduce `NA`s and not return an error anymore
 * Fix for determining the system's language
@@ -444,7 +685,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 * Added Prof. Dr. Casper Albers as doctoral advisor and added Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis Souverein and Annick Lenglet as contributors
 * Cleaned the coding style of every single syntax line in this package with the help of the `lintr` package

-# AMR 0.7.1
+# `AMR` 0.7.1

 #### 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:
@@ -505,7 +746,7 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 #### Other
 * Fixed a note thrown by CRAN tests

-# AMR 0.7.0
+# `AMR` 0.7.0

 #### New
 * Support for translation of disk diffusion and MIC values to RSI values (i.e. antimicrobial interpretations). Supported guidelines are EUCAST (2011 to 2019) and CLSI (2011 to 2019). Use `as.rsi()` on an MIC value (created with `as.mic()`), a disk diffusion value (created with the new `as.disk()`) or on a complete date set containing columns with MIC or disk diffusion values.
@@ -523,15 +764,15 @@ This software is now out of beta and considered stable. Nonetheless, this packag
  * Based on the Compound ID, almost 5,000 official brand names have been added from many different countries
  * All references to antibiotics in our package now use EARS-Net codes, like `AMX` for amoxicillin
  * Functions `atc_certe`, `ab_umcg` and `atc_trivial_nl` have been removed
-  * All `atc_*` functions are superceded by `ab_*` functions
+  * All `atc_*` functions are superseded by `ab_*` functions
  * All output will be translated by using an included translation file which [can be viewed here](https://github.com/msberends/AMR/blob/master/data-raw/translations.tsv)
 * Improvements to plotting AMR results with `ggplot_rsi()`:
-  * New parameter `colours` to set the bar colours
-  * New parameters `title`, `subtitle`, `caption`, `x.title` and `y.title` to set titles and axis descriptions
+  * New argument `colours` to set the bar colours
+  * New arguments `title`, `subtitle`, `caption`, `x.title` and `y.title` to set titles and axis descriptions
 * Improved intelligence of looking up antibiotic columns in a data set using `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.
-* The `age()` function gained a new parameter `exact` to determine ages with decimals
+* 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 argument `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 argument `exact` to determine ages with decimals
 * Removed deprecated functions `guess_mo()`, `guess_atc()`, `EUCAST_rules()`, `interpretive_reading()`, `rsi()`
 * Frequency tables (`freq()`):
  * speed improvement for microbial IDs
@@ -563,23 +804,23 @@ This software is now out of beta and considered stable. Nonetheless, this packag
 #### Other
 * Support for R 3.6.0 and later by providing support for [staged install](https://developer.r-project.org/Blog/public/2019/02/14/staged-install/index.html)

-# AMR 0.6.1
+# `AMR` 0.6.1

 #### Changed
 * Fixed a critical bug when using `eucast_rules()` with `verbose = TRUE`
 * Coercion of microbial IDs are now written to the package namespace instead of the user's home folder, to comply with the CRAN policy

-# AMR 0.6.0
+# `AMR` 0.6.0

 **New website!**

 We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.gitlab.io/AMR/) (built with the great [`pkgdown`](https://pkgdown.r-lib.org/))

-* Contains the complete manual of this package and all of its functions with an explanation of their parameters
-* Contains a comprehensive tutorial about how to conduct antimicrobial resistance analysis, import data from WHONET or SPSS and many more.
+* Contains the complete manual of this package and all of its functions with an explanation of their arguments
+* Contains a comprehensive tutorial about how to conduct AMR data analysis, import data from WHONET or SPSS and many more.

 #### New
-* **BREAKING**: removed deprecated functions, parameters and references to 'bactid'. Use `as.mo()` to identify an MO code.
+* **BREAKING**: removed deprecated functions, arguments and references to 'bactid'. Use `as.mo()` to identify an MO code.
 * Catalogue of Life as a new taxonomic source for data about microorganisms, which also contains all ITIS data we used previously. The `microorganisms` data set now contains:
  * All ~55,000 (sub)species from the kingdoms of Archaea, Bacteria and Protozoa
  * All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales (covering at least like all species of *Aspergillus*, *Candida*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*)
@@ -592,7 +833,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  * New function `mo_rank()` for the taxonomic rank (genus, species, infraspecies, etc.)
  * New function `mo_url()` to get the direct URL of a species from the Catalogue of Life
 * Support for data from [WHONET](https://whonet.org/) and [EARS-Net](https://www.ecdc.europa.eu/en/about-us/partnerships-and-networks/disease-and-laboratory-networks/ears-net) (European Antimicrobial Resistance Surveillance Network):
-  * Exported files from WHONET can be read and used in this package. For functions like `first_isolate()` and `eucast_rules()`, all parameters will be filled in automatically.
+  * Exported files from WHONET can be read and used in this package. For functions like `first_isolate()` and `eucast_rules()`, all arguments will be filled in automatically.
  * This package now knows all antibiotic abbrevations by EARS-Net (which are also being used by WHONET) - the `antibiotics` data set now contains a column `ears_net`.
  * The function `as.mo()` now knows all WHONET species abbreviations too, because almost 2,000 microbial abbreviations were added to the `microorganisms.codes` data set.
 * New filters for antimicrobial classes. Use these functions to filter isolates on results in one of more antibiotics from a specific class:
@@ -635,7 +876,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * New function `mo_uncertainties()` to review values that could be coerced to a valid MO code using `as.mo()`, but with uncertainty.
 * New function `mo_renamed()` to get a list of all returned values from `as.mo()` that have had taxonomic renaming
 * New function `age()` to calculate the (patients) age in years
-* New function `age_groups()` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
+* New function `age_groups()` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic AMR data analysis per age group.
 * New function `ggplot_rsi_predict()` as well as the base R `plot()` function can now be used for resistance prediction calculated with `resistance_predict()`:
  ```r
  x <- resistance_predict(septic_patients, col_ab = "amox")
@@ -665,7 +906,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  * Emphasised in manual that penicillin is meant as benzylpenicillin (ATC [J01CE01](https://www.whocc.no/atc_ddd_index/?code=J01CE01))
  * New info is returned when running this function, stating exactly what has been changed or added. Use `eucast_rules(..., verbose = TRUE)` to get a data set with all changed per bug and drug combination.
 * Removed data sets `microorganisms.oldDT`, `microorganisms.prevDT`, `microorganisms.unprevDT` and `microorganismsDT` since they were no longer needed and only contained info already available in the `microorganisms` data set
-* Added 65 antibiotics to the `antibiotics` data set, from the [Pharmaceuticals Community Register](http://ec.europa.eu/health/documents/community-register/html/atc.htm) of the European Commission
+* Added 65 antibiotics to the `antibiotics` data set, from the [Pharmaceuticals Community Register](https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm) of the European Commission
 * Removed columns `atc_group1_nl` and `atc_group2_nl` from the `antibiotics` data set
 * Functions `atc_ddd()` and `atc_groups()` have been renamed `atc_online_ddd()` and `atc_online_groups()`. The old functions are deprecated and will be removed in a future version.
 * Function `guess_mo()` is now deprecated in favour of `as.mo()` and will be removed in future versions
@@ -713,14 +954,14 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  * Console will return the percentage of uncoercable input
 * Function `first_isolate()`:
  * Fixed a bug where distances between dates would not be calculated right - in the `septic_patients` data set this yielded a difference of 0.15% more isolates
-  * Will now use a column named like "patid" for the patient ID (parameter `col_patientid`), when this parameter was left blank
-  * Will now use a column named like "key(...)ab" or "key(...)antibiotics" for the key antibiotics (parameter `col_keyantibiotics()`), when this parameter was left blank
-  * Removed parameter `output_logical`, the function will now always return a logical value
-  * Renamed parameter `filter_specimen` to `specimen_group`, although using `filter_specimen` will still work
-* A note to the manual pages of the `portion` functions, that low counts can influence the outcome and that the `portion` functions may camouflage this, since they only return the portion (albeit being dependent on the `minimum` parameter)
+  * Will now use a column named like "patid" for the patient ID (argument `col_patientid`), when this argument was left blank
+  * Will now use a column named like "key(...)ab" or "key(...)antibiotics" for the key antibiotics (argument `col_keyantibiotics()`), when this argument was left blank
+  * Removed argument `output_logical`, the function will now always return a logical value
+  * Renamed argument `filter_specimen` to `specimen_group`, although using `filter_specimen` will still work
+* A note to the manual pages of the `portion` functions, that low counts can influence the outcome and that the `portion` functions may camouflage this, since they only return the portion (albeit being dependent on the `minimum` argument)
 * Merged data sets `microorganisms.certe` and `microorganisms.umcg` into `microorganisms.codes`
 * Function `mo_taxonomy()` now contains the kingdom too
-* Reduce false positives for `is.rsi.eligible()` using the new `threshold` parameter
+* Reduce false positives for `is.rsi.eligible()` using the new `threshold` argument
 * New colours for `scale_rsi_colours()`
 * Summaries of class `mo` will now return the top 3 and the unique count, e.g. using `summary(mo)`
 * Small text updates to summaries of class `rsi` and `mic`
@@ -745,16 +986,16 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
      freq(mo_genus(mo))
    ```
  * Header info is now available as a list, with the `header` function
-  * The parameter `header` is now set to `TRUE` at default, even for markdown
+  * The argument `header` is now set to `TRUE` at default, even for markdown
  * Added header info for class `mo` to show unique count of families, genera and species
  * Now honours the `decimal.mark` setting, which just like `format` defaults to `getOption("OutDec")`
-  * The new `big.mark` parameter will at default be `","` when `decimal.mark = "."` and `"."` otherwise
+  * The new `big.mark` argument will at default be `","` when `decimal.mark = "."` and `"."` otherwise
  * Fix for header text where all observations are `NA`
-  * New parameter `droplevels` to exclude empty factor levels when input is a factor
+  * New argument `droplevels` to exclude empty factor levels when input is a factor
  * Factor levels will be in header when present in input data (maximum of 5)
  * Fix for using `select()` on frequency tables
-* Function `scale_y_percent()` now contains the `limits` parameter
-* Automatic parameter filling for `mdro()`, `key_antibiotics()` and `eucast_rules()`
+* Function `scale_y_percent()` now contains the `limits` argument
+* Automatic argument filling for `mdro()`, `key_antibiotics()` and `eucast_rules()`
 * Updated examples for resistance prediction (`resistance_predict()` function)
 * Fix for `as.mic()` to support more values ending in (several) zeroes
 * if using different lengths of pattern and x in `%like%`, it will now return the call
@@ -762,12 +1003,12 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 #### Other
 * Updated licence text to emphasise GPL 2.0 and that this is an R package.

-# AMR 0.5.0
+# `AMR` 0.5.0

 #### New
 * Repository moved to GitLab
 * Function `count_all` to get all available isolates (that like all `portion_*` and `count_*` functions also supports `summarise` and `group_by`), the old `n_rsi` is now an alias of `count_all`
-* Function `get_locale` to determine language for language-dependent output for some `mo_*` functions. This is now the default value for their `language` parameter, by which the system language will be used at default.
+* Function `get_locale` to determine language for language-dependent output for some `mo_*` functions. This is now the default value for their `language` argument, by which the system language will be used at default.
 * Data sets `microorganismsDT`, `microorganisms.prevDT`, `microorganisms.unprevDT` and `microorganisms.oldDT` to improve the speed of `as.mo`. They are for reference only, since they are primarily for internal use of `as.mo`.
 * Function `read.4D` to read from the 4D database of the MMB department of the UMCG
 * Functions `mo_authors` and `mo_year` to get specific values about the scientific reference of a taxonomic entry
@@ -777,12 +1018,12 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * `EUCAST_rules` was renamed to `eucast_rules`, the old function still exists as a deprecated function
 * Big changes to the `eucast_rules` function:
  * Now also applies rules from the EUCAST 'Breakpoint tables for bacteria', version 8.1, 2018, https://www.eucast.org/clinical_breakpoints/ (see Source of the function)
-  * New parameter `rules` to specify which rules should be applied (expert rules, breakpoints, others or all)
-  * New parameter `verbose` which can be set to `TRUE` to get very specific messages about which columns and rows were affected
+  * New argument `rules` to specify which rules should be applied (expert rules, breakpoints, others or all)
+  * New argument `verbose` which can be set to `TRUE` to get very specific messages about which columns and rows were affected
  * Better error handling when rules cannot be applied (i.e. new values could not be inserted)
  * The number of affected values will now only be measured once per row/column combination
  * Data set `septic_patients` now reflects these changes
-  * Added parameter `pipe` for piperacillin (J01CA12), also to the `mdro` function
+  * Added argument `pipe` for piperacillin (J01CA12), also to the `mdro` function
  * Small fixes to EUCAST clinical breakpoint rules
 * Added column `kingdom` to the microorganisms data set, and function `mo_kingdom` to look up values
 * Tremendous speed improvement for `as.mo` (and subsequently all `mo_*` functions), as empty values wil be ignored *a priori*
@@ -794,10 +1035,10 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  as.mo("S. spp")            # B_STPHY
  mo_fullname("S. species")  # "Staphylococcus species"
  ```
-* Added parameter `combine_IR` (TRUE/FALSE) to functions `portion_df` and `count_df`, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
+* Added argument `combine_IR` (TRUE/FALSE) to functions `portion_df` and `count_df`, to indicate that all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)
 * Fix for `portion_*(..., as_percent = TRUE)` when minimal number of isolates would not be met
-* Added parameter `also_single_tested` for `portion_*` and `count_*` functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see `?portion`
-* Using `portion_*` functions now throws a warning when total available isolate is below parameter `minimum`
+* Added argument `also_single_tested` for `portion_*` and `count_*` functions to also include cases where not all antibiotics were tested but at least one of the tested antibiotics includes the target antimicribial interpretation, see `?portion`
+* Using `portion_*` functions now throws a warning when total available isolate is below argument `minimum`
 * Functions `as.mo`, `as.rsi`, `as.mic`, `as.atc` and `freq` will not set package name as attribute anymore
 * Frequency tables - `freq()`:
  * Support for grouping variables, test with:
@@ -816,17 +1057,17 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  * Now prints in markdown at default in non-interactive sessions
  * No longer adds the factor level column and sorts factors on count again
  * Support for class `difftime`
-  * New parameter `na`, to choose which character to print for empty values
-  * New parameter `header` to turn the header info off (default when `markdown = TRUE`)
-  * New parameter `title` to manually setbthe title of the frequency table
-* `first_isolate` now tries to find columns to use as input when parameters are left blank
+  * New argument `na`, to choose which character to print for empty values
+  * New argument `header` to turn the header info off (default when `markdown = TRUE`)
+  * New argument `title` to manually setbthe title of the frequency table
+* `first_isolate` now tries to find columns to use as input when arguments are left blank
 * Improvements for MDRO algorithm (function `mdro`)
 * Data set `septic_patients` is now a `data.frame`, not a tibble anymore
 * Removed diacritics from all authors (columns `microorganisms$ref` and `microorganisms.old$ref`) to comply with CRAN policy to only allow ASCII characters
 * Fix for `mo_property` not working properly
 * Fix for `eucast_rules` where some Streptococci would become ceftazidime R in EUCAST rule 4.5
 * Support for named vectors of class `mo`, useful for `top_freq()`
-* `ggplot_rsi` and `scale_y_percent` have `breaks` parameter
+* `ggplot_rsi` and `scale_y_percent` have `breaks` argument
 * AI improvements for `as.mo`:
  * `"CRS"` -> *Stenotrophomonas maltophilia*
  * `"CRSM"` -> *Stenotrophomonas maltophilia*
@@ -845,7 +1086,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Updated vignettes to comply with README


-# AMR 0.4.0
+# `AMR` 0.4.0

 #### New
 * The data set `microorganisms` now contains **all microbial taxonomic data from ITIS** (kingdoms Bacteria, Fungi and Protozoa), the Integrated Taxonomy Information System, available via https://itis.gov. The data set now contains more than 18,000 microorganisms with all known bacteria, fungi and protozoa according ITIS with genus, species, subspecies, family, order, class, phylum and subkingdom. The new data set `microorganisms.old` contains all previously known taxonomic names from those kingdoms.
@@ -893,7 +1134,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  #         min       median         max  neval
  #  0.01817717  0.01843957  0.03878077    100
  ```
-* Added parameter `reference_df` for `as.mo`, so users can supply their own microbial IDs, name or codes as a reference table
+* Added argument `reference_df` for `as.mo`, so users can supply their own microbial IDs, name or codes as a reference table
 * Renamed all previous references to `bactid` to `mo`, like:
  * Column names inputs of `EUCAST_rules`, `first_isolate` and `key_antibiotics`
  * Column names of datasets `microorganisms` and `septic_patients`
@@ -922,7 +1163,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Fix for `as.mic` for values ending in zeroes after a real number
 * Small fix where *B. fragilis* would not be found in the `microorganisms.umcg` data set
 * Added `prevalence` column to the `microorganisms` data set
-* Added parameters `minimum` and `as_percent` to `portion_df`
+* Added arguments `minimum` and `as_percent` to `portion_df`
 * Support for quasiquotation in the functions series `count_*` and `portions_*`, and `n_rsi`. This allows to check for more than 2 vectors or columns.
  ```r
  septic_patients %>% select(amox, cipr) %>% count_IR()
@@ -933,12 +1174,12 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  septic_patients %>% portion_S(amcl, gent)
  septic_patients %>% portion_S(amcl, gent, pita)
  ```
-* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
+* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` argument has value `portion_df` at default, but can be set to `count_df`.
 * Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
 * Added datalabels function `labels_rsi_count` to `ggplot_rsi`
-* Added possibility to set any parameter to `geom_rsi` (and `ggplot_rsi`) so you can set your own preferences
+* Added possibility to set any argument to `geom_rsi` (and `ggplot_rsi`) so you can set your own preferences
 * Fix for joins, where predefined suffices would not be honoured
-* Added parameter `quote` to the `freq` function
+* Added argument `quote` to the `freq` function
 * Added generic function `diff` for frequency tables
 * Added longest en shortest character length in the frequency table (`freq`) header of class `character`
 * Support for types (classes) list and matrix for `freq`
@@ -956,7 +1197,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 #### Other
 * More unit tests to ensure better integrity of functions

-# AMR 0.3.0
+# `AMR` 0.3.0

 #### New
 * **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old `rsi` function. The old function still works, but is deprecated.
@@ -995,7 +1236,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git

 #### Changed
 * Improvements for forecasting with `resistance_predict` and added more examples
-* More antibiotics added as parameters for EUCAST rules
+* More antibiotics added as arguments for EUCAST rules
 * Updated version of the `septic_patients` data set to better reflect the reality
 * Pretty printing for tibbles removed as it is not really the scope of this package
 * Printing of `mic` and `rsi` classes now returns all values - use `freq` to check distributions
@@ -1003,7 +1244,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Column names for the `key_antibiotics` function are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
 * Speed improvement for the `abname` function
 * `%like%` now supports multiple patterns
-* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
+* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the argument `toConsole` is not longer needed.
 * Fix for `freq` where the class of an item would be lost
 * Small translational improvements to the `septic_patients` dataset and the column `bactid` now has the new class `"bactid"`
 * Small improvements to the `microorganisms` dataset (especially for *Salmonella*) and the column `bactid` now has the new class `"bactid"`
@@ -1026,7 +1267,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
  * Windows: https://ci.appveyor.com/project/msberends/amr
 * Added thesis advisors to DESCRIPTION file

-# AMR 0.2.0
+# `AMR` 0.2.0

 #### New
 * Full support for Windows, Linux and macOS
@@ -1051,7 +1292,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Added support for character vector in `join` functions
 * Added warnings when a join results in more rows after than before the join
 * Altered `%like%` to make it case insensitive
-* For parameters of functions `first_isolate` and `EUCAST_rules` column names are now case-insensitive
+* For arguments of functions `first_isolate` and `EUCAST_rules` column names are now case-insensitive
 * Functions `as.rsi` and `as.mic` now add the package name and version as attributes

 #### Other
@@ -1061,7 +1302,7 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Added build tests for Linux and macOS using Travis CI (https://travis-ci.org/msberends/AMR)
 * Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/master/R)

-# AMR 0.1.1
+# `AMR` 0.1.1

 * `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
 * Edited column names to comply with GLIMS, the laboratory information system
@@ -1069,6 +1310,6 @@ We've got a new website: [https://msberends.gitlab.io/AMR](https://msberends.git
 * Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
 * Added barplots for `rsi` and `mic` classes

-# AMR 0.1.0
+# `AMR` 0.1.0

 * First submission to CRAN.
--- a/R/aa_helper_functions.R
+++ b/R/aa_helper_functions.R
--- a/R/aa_helper_pm_functions.R
+++ b/R/aa_helper_pm_functions.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 # ------------------------------------------------
@@ -388,29 +388,29 @@ pm_group_size <- function(x) {
 pm_n_groups <- function(x) {
  nrow(pm_group_data(x))
 }
-pm_group_split <- function(.data, ..., .keep = TRUE) {
-  dots_len <- ...length() > 0L
-  if (pm_has_groups(.data) && isTRUE(dots_len)) {
-    warning("... is ignored in pm_group_split(<grouped_df>), please use pm_group_by(..., .add = TRUE) %pm>% pm_group_split()")
-  }
-  if (!pm_has_groups(.data) && isTRUE(dots_len)) {
-    .data <- pm_group_by(.data, ...)
-  }
-  if (!pm_has_groups(.data) && isFALSE(dots_len)) {
-    return(list(.data))
-  }
-  pm_context$setup(.data)
-  on.exit(pm_context$clean(), add = TRUE)
-  pm_groups <- pm_get_groups(.data)
-  attr(pm_context$.data, "pm_groups") <- NULL
-  res <- pm_split_into_groups(pm_context$.data, pm_groups)
-  names(res) <- NULL
-  if (isFALSE(.keep)) {
-    res <- lapply(res, function(x) x[, !colnames(x) %in% pm_groups])
-  }
-  any_empty <- unlist(lapply(res, function(x) !(nrow(x) == 0L)))
-  res[any_empty]
-}
+# pm_group_split <- function(.data, ..., .keep = TRUE) {
+#   dots_len <- ...length() > 0L
+#   if (pm_has_groups(.data) && isTRUE(dots_len)) {
+#     warning("... is ignored in pm_group_split(<grouped_df>), please use pm_group_by(..., .add = TRUE) %pm>% pm_group_split()")
+#   }
+#   if (!pm_has_groups(.data) && isTRUE(dots_len)) {
+#     .data <- pm_group_by(.data, ...)
+#   }
+#   if (!pm_has_groups(.data) && isFALSE(dots_len)) {
+#     return(list(.data))
+#   }
+#   pm_context$setup(.data)
+#   on.exit(pm_context$clean(), add = TRUE)
+#   pm_groups <- pm_get_groups(.data)
+#   attr(pm_context$.data, "pm_groups") <- NULL
+#   res <- pm_split_into_groups(pm_context$.data, pm_groups)
+#   names(res) <- NULL
+#   if (isFALSE(.keep)) {
+#     res <- lapply(res, function(x) x[, !colnames(x) %in% pm_groups])
+#   }
+#   any_empty <- unlist(lapply(res, function(x) !(nrow(x) == 0L)))
+#   res[any_empty]
+# }

 pm_group_keys <- function(.data) {
  pm_groups <- pm_get_groups(.data)
--- a/R/ab.R
+++ b/R/ab.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,16 +20,16 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Transform input to an antibiotic ID
+#' Transform Input to an Antibiotic ID
 #'
 #' 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).
-#' @inheritSection lifecycle Stable lifecycle
-#' @param x character vector to determine to antibiotic ID
-#' @param flag_multiple_results logical to indicate whether a note should be printed to the console that probably more than one antibiotic code or name can be retrieved from a single input value.
-#' @param info logical to indicate whether a progress bar should be printed
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x a [character] vector to determine to antibiotic ID
+#' @param flag_multiple_results a [logical] to indicate whether a note should be printed to the console that probably more than one antibiotic code or name can be retrieved from a single input value.
+#' @param info a [logical] to indicate whether a progress bar should be printed, defaults to `TRUE` only in interactive mode
 #' @param ... arguments passed on to internal functions
 #' @rdname as.ab
 #' @inheritSection WHOCC WHOCC
@@ -37,26 +37,27 @@
 #' 
 #' All these properties will be searched for the user input. The [as.ab()] can correct for different forms of misspelling:
 #' 
-#'  * Wrong spelling of drug names (like "tobramicin" or "gentamycin"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.
+#'  * Wrong spelling of drug names (such as "tobramicin" or "gentamycin"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.
 #'  * Too few or too many vowels or consonants
-#'  * Switching two characters (like "mreopenem", often the case in clinical data, when doctors typed too fast)
+#'  * Switching two characters (such as "mreopenem", often the case in clinical data, when doctors typed too fast)
 #'  * Digitalised paper records, leaving artefacts like 0/o/O (zero and O's), B/8, n/r, etc.
 #'
-#' Use the [ab_property()] functions to get properties based on the returned antibiotic ID, see Examples.
+#' Use the [`ab_*`][ab_property()] functions to get properties based on the returned antibiotic ID, see *Examples*.
 #' 
+#' Note: the [as.ab()] and [`ab_*`][ab_property()] functions may use very long regular expression to match brand names of antimicrobial agents. This may fail on some systems.
 #' @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}
+#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}
 #' @aliases ab
 #' @return A [character] [vector] with additional class [`ab`]
 #' @seealso 
 #' * [antibiotics] for the [data.frame] that is being used to determine ATCs
 #' * [ab_from_text()] for a function to retrieve antimicrobial drugs from clinical text (from health care records)
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 #' @examples
 #' # these examples all return "ERY", the ID of erythromycin:
@@ -81,7 +82,19 @@
 #' # they use as.ab() internally:
 #' ab_name("J01FA01")    # "Erythromycin"
 #' ab_name("eryt")       # "Erythromycin"
-as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
+#' \donttest{
+#' if (require("dplyr")) {
+#' 
+#'   # you can quickly rename <rsi> columns using dplyr >= 1.0.0:
+#'   example_isolates %>%
+#'     rename_with(as.ab, where(is.rsi))
+#'    
+#' }
+#' }
+as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
+  meet_criteria(x, allow_class = c("character", "numeric", "integer", "factor"), allow_NA = TRUE)
+  meet_criteria(flag_multiple_results, allow_class = "logical", has_length = 1)
+  meet_criteria(info, allow_class = "logical", has_length = 1)
  
  check_dataset_integrity()
  
@@ -91,19 +104,34 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
  
  initial_search <- is.null(list(...)$initial_search)
  already_regex <- isTRUE(list(...)$already_regex)
-  
-  if (all(toupper(x) %in% antibiotics$ab)) {
-    # valid AB code, but not yet right class
-    return(structure(.Data = toupper(x),
-                     class = c("ab", "character")))
-  }
+  fast_mode <- isTRUE(list(...)$fast_mode)
  
  x_bak <- x
  x <- toupper(x)
+  x_nonNA <- x[!is.na(x)]
+  
+  if (all(x_nonNA %in% antibiotics$ab, na.rm = TRUE)) {
+    # all valid AB codes, but not yet right class
+    return(set_clean_class(x,
+                           new_class = c("ab", "character")))
+  }
+  if (all(x_nonNA %in% toupper(antibiotics$name), na.rm = TRUE)) {
+    # all valid AB names
+    out <- antibiotics$ab[match(x, toupper(antibiotics$name))]
+    out[is.na(x)] <- NA_character_
+    return(out)
+  }
+  if (all(x_nonNA %in% antibiotics$atc, na.rm = TRUE)) {
+    # all valid ATC codes
+    out <- antibiotics$ab[match(x, antibiotics$atc)]
+    out[is.na(x)] <- NA_character_
+    return(out)
+  }
+  
  # remove diacritics
  x <- iconv(x, from = "UTF-8", to = "ASCII//TRANSLIT")
  x <- gsub('"', "", x, fixed = TRUE)
-  x <- gsub("(specimen|specimen date|specimen_date|spec_date)", "", x, ignore.case = TRUE, perl = TRUE)
+  x <- gsub("(specimen|specimen date|specimen_date|spec_date|^dates?$)", "", x, ignore.case = TRUE, perl = TRUE)
  x_bak_clean <- x
  if (already_regex == FALSE) {
    x_bak_clean <- generalise_antibiotic_name(x_bak_clean)
@@ -120,19 +148,20 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
        abnames <- abnames[!abnames == "clavulanic acid"]
      }
      if (length(abnames) > 1) {
-        message(font_blue(paste0("NOTE: more than one result was found for item ", index, ": ",
-                                 paste0(abnames, collapse = ", "))))
+        message_("More than one result was found for item ", index, ": ",
+                 vector_and(abnames, quotes = FALSE))
      }
    }
    found[1L]
  }
  
  if (initial_search == TRUE) {
-    progress <- progress_ticker(n = length(x), n_min = ifelse(isTRUE(info), 25, length(x) + 1)) # start if n >= 25
+    progress <- progress_ticker(n = length(x), n_min = 25, print = info) # start if n >= 25
    on.exit(close(progress))
  }
  
  for (i in seq_len(length(x))) {
+
    if (initial_search == TRUE) {
      progress$tick()
    }
@@ -141,20 +170,25 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
      next
    }
    if (identical(x[i], "") |
-        # no short names:
-        nchar(x[i]) <= 2 |
        # prevent "bacteria" from coercing to TMP, since Bacterial is a brand name of it:
        identical(tolower(x[i]), "bacteria")) {
      x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
      next
    }
    
-    if (isTRUE(flag_multiple_results) & x[i] %like% "[ ]") {
-      from_text <- suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]])
+    if (fast_mode == FALSE && flag_multiple_results == TRUE && x[i] %like% "[ ]") {
+      from_text <- tryCatch(suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]]),
+                            error = function(e) character(0))
    } else {
      from_text <- character(0)
    }
    
+    # old code for phenoxymethylpenicillin (Peni V)
+    if (x[i] == "PNV") {
+      x_new[i] <- "PHN"
+      next
+    }
+    
    # exact name
    found <- antibiotics[which(AB_lookup$generalised_name == x[i]), ]$ab
    if (length(found) > 0) {
@@ -203,7 +237,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
    
    # exact abbreviation
    abbr_found <- unlist(lapply(AB_lookup$generalised_abbreviations,
-                                function(s) x[i] %in% s))
+                                # require at least 2 characters for abbreviations
+                                function(s) x[i] %in% s & nchar(x[i]) >= 2))
    found <- antibiotics$ab[abbr_found == TRUE]
    if (length(found) > 0) {
      x_new[i] <- note_if_more_than_one_found(found, i, from_text)
@@ -263,8 +298,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
    }
    
    # INITIAL SEARCH - More uncertain results ----
-    
-    if (initial_search == TRUE) {
+
+    if (initial_search == TRUE && fast_mode == FALSE) {
      # only run on first try
      
      # try by removing all spaces
@@ -289,10 +324,12 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
      x_translated <- paste(lapply(strsplit(x[i], "[^A-Z0-9]"),
                                   function(y) {
                                     for (i in seq_len(length(y))) {
-                                       y[i] <- ifelse(tolower(y[i]) %in% tolower(translations_file$replacement),
-                                                      translations_file[which(tolower(translations_file$replacement) == tolower(y[i]) &
-                                                                                !isFALSE(translations_file$fixed)), "pattern"],
-                                                      y[i])
+                                       for (lang in LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED != "en"]) {
+                                         y[i] <- ifelse(tolower(y[i]) %in% tolower(translations_file[, lang, drop = TRUE]),
+                                                        translations_file[which(tolower(translations_file[, lang, drop = TRUE]) == tolower(y[i]) &
+                                                                                  !isFALSE(translations_file$fixed)), "pattern"],
+                                                        y[i])
+                                       }
                                     }
                                     generalise_antibiotic_name(y)
                                   })[[1]],
@@ -339,10 +376,11 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
      
      # try from a bigger text, like from a health care record, see ?ab_from_text
      # already calculated above if flag_multiple_results = TRUE
-      if (isTRUE(flag_multiple_results)) {
+      if (flag_multiple_results == TRUE) {
        found <- from_text[1L]
      } else {
-        found <- suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]][1L])
+        found <- tryCatch(suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]][1L]),
+                          error = function(e) NA_character_)
      }
      if (!is.na(found)) {
        x_new[i] <- note_if_more_than_one_found(found, i, from_text)
@@ -351,7 +389,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
      
      # first 5 except for cephalosporins, then first 7 (those cephalosporins all start quite the same!)
      found <- suppressWarnings(as.ab(substr(x[i], 1, 5), initial_search = FALSE))
-      if (!is.na(found) && !ab_group(found, initial_search = FALSE) %like% "cephalosporins") {
+      if (!is.na(found) && ab_group(found, initial_search = FALSE) %unlike% "cephalosporins") {
        x_new[i] <- note_if_more_than_one_found(found, i, from_text)
        next
      }
@@ -431,29 +469,27 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = TRUE, ...) {
  x_unknown_ATCs <- x_unknown[x_unknown %like% "[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]"]
  x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
  if (length(x_unknown_ATCs) > 0) {
-    warning("These ATC codes are not (yet) in the antibiotics data set: ",
-            paste('"', sort(unique(x_unknown_ATCs)), '"', sep = "", collapse = ", "),
-            ".",
-            call. = FALSE)
+    warning_("These ATC codes are not (yet) in the antibiotics data set: ",
+             vector_and(x_unknown_ATCs), ".",
+             call = FALSE)
  }
  
-  if (length(x_unknown) > 0) {
-    warning("These values could not be coerced to a valid antimicrobial ID: ",
-            paste('"', sort(unique(x_unknown)), '"', sep = "", collapse = ", "),
-            ".",
-            call. = FALSE)
+  if (length(x_unknown) > 0 & fast_mode == FALSE) {
+    warning_("These values could not be coerced to a valid antimicrobial ID: ",
+             vector_and(x_unknown), ".",
+             call = FALSE)
  }
  
  x_result <- data.frame(x = x_bak_clean, stringsAsFactors = FALSE) %pm>%
    pm_left_join(data.frame(x = x, x_new = x_new, stringsAsFactors = FALSE), by = "x") %pm>%
-   pm_pull(x_new)
+    pm_pull(x_new)
  
  if (length(x_result) == 0) {
    x_result <- NA_character_
  }
  
-  structure(.Data = x_result,
-            class = c("ab", "character"))
+  set_clean_class(x_result,
+                  new_class = c("ab", "character"))
 }

 #' @rdname as.ab
@@ -515,7 +551,7 @@ as.data.frame.ab <- function(x, ...) {
 "[<-.ab" <- function(i, j, ..., value) {
  y <- NextMethod()
  attributes(y) <- attributes(i)
-  class_integrity_check(y, "antimicrobial code", antibiotics$ab)
+  return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
 }
 #' @method [[<- ab
 #' @export
@@ -523,15 +559,16 @@ as.data.frame.ab <- function(x, ...) {
 "[[<-.ab" <- function(i, j, ..., value) {
  y <- NextMethod()
  attributes(y) <- attributes(i)
-  class_integrity_check(y, "antimicrobial code", antibiotics$ab)
+  return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
 }
 #' @method c ab
 #' @export
 #' @noRd
-c.ab <- function(x, ...) {
+c.ab <- function(...) {
+  x <- list(...)[[1L]]
  y <- NextMethod()
  attributes(y) <- attributes(x)
-  class_integrity_check(y, "antimicrobial code", antibiotics$ab)
+  return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
 }

 #' @method unique ab
--- a/R/ab_class_selectors.R
+++ b/R/ab_class_selectors.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,23 +20,57 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Antibiotic class selectors
+#' Antibiotic Class Selectors
 #' 
-#' Use these selection helpers inside any function that allows [Tidyverse selection helpers](https://tidyselect.r-lib.org/reference/language.html), like `dplyr::select()` or `tidyr::pivot_longer()`. They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
-#' @inheritParams filter_ab_class 
-#' @details All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.) in the [antibiotics] data set. This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
+#' These functions help to filter and select columns with antibiotic test results that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations. \strong{\Sexpr{ifelse(getRversion() < "3.2", paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param ab_class an antimicrobial class, such as `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
+#' @param only_rsi_columns a [logical] to indicate whether only columns of class `<rsi>` must be selected (defaults to `FALSE`), see [as.rsi()]
+#' @details \strong{\Sexpr{ifelse(getRversion() < "3.2", paste0("NOTE: THESE FUNCTIONS DO NOT WORK ON YOUR CURRENT R VERSION. These functions require R version 3.2 or later - you have ", R.version.string, "."), "")}}
 #' 
-#' **N.B. These functions only work if the `tidyselect` package is installed**, that comes with the `dplyr` package. An error will be thrown if the `tidyselect` package is not installed, or if the functions are used outside a function that allows Tidyverse selections like `select()` or `pivot_longer()`.
+#' 
+#' These functions can be used in data set calls for selecting columns and filtering rows, see *Examples*. They support base R, but work more convenient in dplyr functions such as [`select()`][dplyr::select()], [`filter()`][dplyr::filter()] and [`summarise()`][dplyr::summarise()].
+#' 
+#' All columns in the data in which these functions are called will be searched for known antibiotic names, abbreviations, brand names, and codes (ATC, EARS-Net, WHO, etc.) in the [antibiotics] data set. This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
+#' 
+#' The group of betalactams consists of all carbapenems, cephalosporins and penicillins.
 #' @rdname antibiotic_class_selectors
-#' @seealso [filter_ab_class()] for the `filter()` equivalent.
 #' @name antibiotic_class_selectors
 #' @export
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @examples 
+#' # `example_isolates` is a data set available in the AMR package.
+#' # See ?example_isolates.
+#' 
+#' # Base R ------------------------------------------------------------------
+#' 
+#' # select columns 'IPM' (imipenem) and 'MEM' (meropenem)
+#' example_isolates[, carbapenems()]
+#' 
+#' # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB'
+#' example_isolates[, c("mo", aminoglycosides())]
+#' 
+#' # filter using any() or all()
+#' example_isolates[any(carbapenems() == "R"), ]
+#' subset(example_isolates, any(carbapenems() == "R"))
+#' 
+#' # filter on any or all results in the carbapenem columns (i.e., IPM, MEM):
+#' example_isolates[any(carbapenems()), ]
+#' example_isolates[all(carbapenems()), ]
+#' 
+#' # filter with multiple antibiotic selectors using c()
+#' example_isolates[all(c(carbapenems(), aminoglycosides()) == "R"), ]
+#' 
+#' # filter + select in one go: get penicillins in carbapenems-resistant strains
+#' example_isolates[any(carbapenems() == "R"), penicillins()]
+#' 
+#' 
+#' # dplyr -------------------------------------------------------------------
+#' \donttest{
 #' if (require("dplyr")) {
 #' 
 #'   # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
@@ -47,6 +81,20 @@
 #'   example_isolates %>% 
 #'     select(mo, aminoglycosides())
 #'     
+#'  # any() and all() work in dplyr's filter() too:
+#'  example_isolates %>% 
+#'     filter(any(aminoglycosides() == "R"),
+#'            all(cephalosporins_2nd() == "R"))
+#'     
+#'  # also works with c():
+#'  example_isolates %>% 
+#'     filter(any(c(carbapenems(), aminoglycosides()) == "R"))
+#'     
+#'  # not setting any/all will automatically apply all():
+#'  example_isolates %>% 
+#'     filter(aminoglycosides() == "R")
+#'  #> i Assuming a filter on all 4 aminoglycosides.
+#'     
 #'   # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):
 #'   example_isolates %>% 
 #'     select(mo, ab_class("mycobact"))
@@ -54,7 +102,7 @@
 #'     
 #'   # get bug/drug combinations for only macrolides in Gram-positives:
 #'   example_isolates %>% 
-#'     filter(mo_gramstain(mo) %like% "pos") %>% 
+#'     filter(mo_is_gram_positive()) %>% 
 #'     select(mo, macrolides()) %>% 
 #'     bug_drug_combinations() %>%
 #'     format()
@@ -63,99 +111,129 @@
 #'   data.frame(some_column = "some_value",
 #'              J01CA01 = "S") %>%   # ATC code of ampicillin
 #'     select(penicillins())         # only the 'J01CA01' column will be selected
-#'
+#'     
+#'     
+#'   # with dplyr 1.0.0 and higher (that adds 'across()'), this is all equal:
+#'   # (though the row names on the first are more correct)
+#'   example_isolates[carbapenems() == "R", ]
+#'   example_isolates %>% filter(carbapenems() == "R")
+#'   example_isolates %>% filter(across(carbapenems(), ~.x == "R"))
 #' }
-ab_class <- function(ab_class) {
-  ab_selector(ab_class, function_name = "ab_class")
+#' }
+ab_class <- function(ab_class, 
+                     only_rsi_columns = FALSE) {
+  ab_selector(ab_class, function_name = "ab_class", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-aminoglycosides <- function() {
-  ab_selector("aminoglycoside", function_name = "aminoglycosides")
+aminoglycosides <- function(only_rsi_columns = FALSE) {
+  ab_selector("aminoglycoside", function_name = "aminoglycosides", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-carbapenems <- function() {
-  ab_selector("carbapenem", function_name = "carbapenems")
+betalactams <- function(only_rsi_columns = FALSE) {
+  ab_selector("carbapenem|cephalosporin|penicillin", function_name = "betalactams", only_rsi_columns = only_rsi_columns)
+}
+#' @rdname antibiotic_class_selectors
+#' @export
+carbapenems <- function(only_rsi_columns = FALSE) {
+  ab_selector("carbapenem", function_name = "carbapenems", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins <- function() {
-  ab_selector("cephalosporin", function_name = "cephalosporins")
+cephalosporins <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporin", function_name = "cephalosporins", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins_1st <- function() {
-  ab_selector("cephalosporins.*1", function_name = "cephalosporins_1st")
+cephalosporins_1st <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporins.*1", function_name = "cephalosporins_1st", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins_2nd <- function() {
-  ab_selector("cephalosporins.*2", function_name = "cephalosporins_2nd")
+cephalosporins_2nd <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporins.*2", function_name = "cephalosporins_2nd", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins_3rd <- function() {
-  ab_selector("cephalosporins.*3", function_name = "cephalosporins_3rd")
+cephalosporins_3rd <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporins.*3", function_name = "cephalosporins_3rd", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins_4th <- function() {
-  ab_selector("cephalosporins.*4", function_name = "cephalosporins_4th")
+cephalosporins_4th <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporins.*4", function_name = "cephalosporins_4th", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-cephalosporins_5th <- function() {
-  ab_selector("cephalosporins.*5", function_name = "cephalosporins_5th")
+cephalosporins_5th <- function(only_rsi_columns = FALSE) {
+  ab_selector("cephalosporins.*5", function_name = "cephalosporins_5th", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-fluoroquinolones <- function() {
-  ab_selector("fluoroquinolone", function_name = "fluoroquinolones")
+fluoroquinolones <- function(only_rsi_columns = FALSE) {
+  ab_selector("fluoroquinolone", function_name = "fluoroquinolones", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-glycopeptides <- function() {
-  ab_selector("glycopeptide", function_name = "glycopeptides")
+glycopeptides <- function(only_rsi_columns = FALSE) {
+  ab_selector("glycopeptide", function_name = "glycopeptides", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-macrolides <- function() {
-  ab_selector("macrolide", function_name = "macrolides")
+macrolides <- function(only_rsi_columns = FALSE) {
+  ab_selector("macrolide", function_name = "macrolides", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-penicillins <- function() {
-  ab_selector("penicillin", function_name = "penicillins")
+oxazolidinones <- function(only_rsi_columns = FALSE) {
+  ab_selector("oxazolidinone", function_name = "oxazolidinones", only_rsi_columns = only_rsi_columns)
 }

 #' @rdname antibiotic_class_selectors
 #' @export
-tetracyclines <- function() {
-  ab_selector("tetracycline", function_name = "tetracyclines")
+penicillins <- function(only_rsi_columns = FALSE) {
+  ab_selector("penicillin", function_name = "penicillins", only_rsi_columns = only_rsi_columns)
 }

-ab_selector <- function(ab_class, function_name) {
-  peek_vars_tidyselect <- import_fn("peek_vars", "tidyselect")
-  vars_vct <- peek_vars_tidyselect(fn = function_name)
-  vars_df <- data.frame(as.list(vars_vct))[0, , drop = FALSE]
-  colnames(vars_df) <- vars_vct
-  ab_in_data <- get_column_abx(vars_df, info = FALSE)
+#' @rdname antibiotic_class_selectors
+#' @export
+tetracyclines <- function(only_rsi_columns = FALSE) {
+  ab_selector("tetracycline", function_name = "tetracyclines", only_rsi_columns = only_rsi_columns)
+}
+
+ab_selector <- function(ab_class,
+                        function_name,
+                        only_rsi_columns) {
+  meet_criteria(ab_class, allow_class = "character", has_length = 1, .call_depth = 1)
+  meet_criteria(function_name, allow_class = "character", has_length = 1, .call_depth = 1)
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1, .call_depth = 1)
+  
+  if (getRversion() < "3.2") {
+    warning_("antibiotic class selectors such as ", function_name, 
+             "() require R version 3.2 or later - you have ", R.version.string,
+             call = FALSE)
+    return(NULL)
+  }
+  
+  # to improve speed, get_current_data() and get_column_abx() only run once when e.g. in a select or group call
+  vars_df <- get_current_data(arg_name = NA, call = -3)
+  ab_in_data <- get_column_abx(vars_df, info = FALSE, only_rsi_columns = only_rsi_columns, sort = FALSE)
  
  if (length(ab_in_data) == 0) {
-    message(font_blue("NOTE: no antimicrobial agents found."))
+    message_("No antimicrobial agents found.")
    return(NULL)
  }
  
@@ -172,14 +250,215 @@ ab_selector <- function(ab_class, function_name) {
  }
  # get the columns with a group names in the chosen ab class
  agents <- ab_in_data[names(ab_in_data) %in% ab_reference$ab]
-  if (length(agents) == 0) {
-    message(font_blue(paste0("NOTE: No antimicrobial agents of class ", ab_group, 
-                             " found", examples, ".")))
-  } else {
-    message(font_blue(paste0("Selecting ", ab_group, ": ",
-                             paste(paste0("`", font_bold(agents, collapse = NULL),
-                                          "` (", ab_name(names(agents), tolower = TRUE, language = NULL), ")"),
-                                   collapse = ", "))))
+    
+  if (message_not_thrown_before(function_name)) {
+    if (length(agents) == 0) {
+      message_("No antimicrobial agents of class ", ab_group, " found", examples, ".")
+    } else {
+      agents_formatted <- paste0("'", font_bold(agents, collapse = NULL), "'")
+      agents_names <- ab_name(names(agents), tolower = TRUE, language = NULL)
+      need_name <- tolower(gsub("[^a-zA-Z]", "", agents)) != tolower(gsub("[^a-zA-Z]", "", agents_names))
+      agents_formatted[need_name] <- paste0(agents_formatted[need_name],
+                                            " (", agents_names[need_name], ")")
+      message_("For `", function_name, "(", ifelse(function_name == "ab_class", paste0("\"", ab_class, "\""), ""), ")` using ",
+               ifelse(length(agents) == 1, "column: ", "columns: "),
+               vector_and(agents_formatted, quotes = FALSE))
+    }
+    remember_thrown_message(function_name)
+  }
+  
+  if (!is.null(attributes(vars_df)$type) &&
+      attributes(vars_df)$type %in% c("dplyr_cur_data_all", "base_R") &&
+      !any(as.character(sys.calls()) %like% paste0("(across|if_any|if_all)\\((c\\()?[a-z(), ]*", function_name))) {
+    structure(unname(agents),
+              class = c("ab_selector", "character"))
+  } else {
+    # don't return with "ab_selector" class if method is a dplyr selector,
+    # dplyr::select() will complain:
+    # > Subscript has the wrong type `ab_selector`.
+    # > It must be numeric or character.
+    unname(agents)
  }
-  unname(agents)
+}
+
+#' @method c ab_selector
+#' @export
+#' @noRd
+c.ab_selector <- function(...) {
+  structure(unlist(lapply(list(...), as.character)),
+            class = c("ab_selector", "character"))
+}
+
+all_any_ab_selector <- function(type, ..., na.rm = TRUE) {
+  cols_ab <- c(...)
+  result <- cols_ab[toupper(cols_ab) %in% c("R", "S", "I")]
+  if (length(result) == 0) {
+    message_("Filtering ", type, " of columns ", vector_and(font_bold(cols_ab, collapse = NULL), quotes = "'"), ' to contain value "R", "S" or "I"')
+    result <- c("R", "S", "I")
+  }
+  cols_ab <- cols_ab[!cols_ab %in% result]
+  df <- get_current_data(arg_name = NA, call = -3)
+  
+  if (type == "all") {
+    scope_fn <- all
+  } else {
+    scope_fn <- any
+  }
+  
+  x_transposed <- as.list(as.data.frame(t(df[, cols_ab, drop = FALSE]), stringsAsFactors = FALSE))
+  vapply(FUN.VALUE = logical(1),
+         X = x_transposed,
+         FUN = function(y) scope_fn(y %in% result, na.rm = na.rm),
+         USE.NAMES = FALSE)
+}
+
+#' @method all ab_selector
+#' @export
+#' @noRd
+all.ab_selector <- function(..., na.rm = FALSE) {
+  # this is all() for 
+  all_any_ab_selector("all", ..., na.rm = na.rm)
+}
+
+#' @method any ab_selector
+#' @export
+#' @noRd
+any.ab_selector <- function(..., na.rm = FALSE) {
+  all_any_ab_selector("any", ..., na.rm = na.rm)
+}
+
+
+#' @method all ab_selector_any_all
+#' @export
+#' @noRd
+all.ab_selector_any_all <- function(..., na.rm = FALSE) {
+  # this is all() on a logical vector from `==.ab_selector` or `!=.ab_selector`
+  # e.g., example_isolates %>% filter(all(carbapenems() == "R"))
+  # so just return the vector as is, only correcting for na.rm
+  out <- unclass(c(...))
+  if (na.rm == TRUE) {
+    out <- out[!is.na(out)]
+  }
+  out
+}
+
+#' @method any ab_selector_any_all
+#' @export
+#' @noRd
+any.ab_selector_any_all <- function(..., na.rm = FALSE) {
+  # this is any() on a logical vector from `==.ab_selector` or `!=.ab_selector`
+  # e.g., example_isolates %>% filter(any(carbapenems() == "R"))
+  # so just return the vector as is, only correcting for na.rm
+  out <- unclass(c(...))
+  if (na.rm == TRUE) {
+    out <- out[!is.na(out)]
+  }
+  out
+}
+
+#' @method == ab_selector
+#' @export
+#' @noRd
+`==.ab_selector` <- function(e1, e2) {
+  calls <- as.character(match.call())
+  fn_name <- calls[2]
+  # keep only the ... in c(...)
+  fn_name <- gsub("^(c\\()(.*)(\\))$", "\\2", fn_name)
+  if (is_any(fn_name)) {
+    type <- "any"
+  } else if (is_all(fn_name)) {
+    type <- "all"
+  } else {
+    type <- "all"
+    if (length(e1) > 1) {
+      message_("Assuming a filter on ", type, " ", length(e1), " ", gsub("[\\(\\)]", "", fn_name),
+               ". Wrap around `all()` or `any()` to prevent this note.")
+    }
+  }
+  structure(all_any_ab_selector(type = type, e1, e2),
+            class = c("ab_selector_any_all", "logical"))
+}
+
+#' @method != ab_selector
+#' @export
+#' @noRd
+`!=.ab_selector` <- function(e1, e2) {
+  calls <- as.character(match.call())
+  fn_name <- calls[2]
+  # keep only the ... in c(...)
+  fn_name <- gsub("^(c\\()(.*)(\\))$", "\\2", fn_name)
+  if (is_any(fn_name)) {
+    type <- "any"
+  } else if (is_all(fn_name)) {
+    type <- "all"
+  } else {
+    type <- "all"
+    if (length(e1) > 1) {
+      message_("Assuming a filter on ", type, " ", length(e1), " ", gsub("[\\(\\)]", "", fn_name),
+               ". Wrap around `all()` or `any()` to prevent this note.")
+    }
+  }
+  # this is `!=`, so turn around the values
+  rsi <- c("R", "S", "I")
+  e2 <- rsi[rsi != e2]
+  structure(all_any_ab_selector(type = type, e1, e2),
+            class = c("ab_selector_any_all", "logical"))
+}
+
+is_any <- function(el1) {
+  syscall <- paste0(trimws(deparse(sys.calls()[[1]])), collapse = " ")
+  el1 <- gsub("(.*),.*", "\\1", el1)
+  syscall %like% paste0("[^_a-zA-Z0-9]any\\(", "(c\\()?", el1)
+}
+is_all <- function(el1) {
+  syscall <- paste0(trimws(deparse(sys.calls()[[1]])), collapse = " ")
+  el1 <- gsub("(.*),.*", "\\1", el1)
+  syscall %like% paste0("[^_a-zA-Z0-9]all\\(", "(c\\()?", el1)
+}
+
+
+find_ab_group <- function(ab_class) {
+  ab_class[ab_class == "carbapenem|cephalosporin|penicillin"] <- "betalactam"
+  ab_class <- gsub("[^a-zA-Z0-9]", ".*", ab_class)
+  ifelse(ab_class %in% c("aminoglycoside",
+                         "betalactam",
+                         "carbapenem",
+                         "cephalosporin",
+                         "fluoroquinolone",
+                         "glycopeptide",
+                         "macrolide",
+                         "oxazolidinone",
+                         "tetracycline"),
+         paste0(ab_class, "s"),
+         antibiotics %pm>%
+           subset(group %like% ab_class | 
+                    atc_group1 %like% ab_class | 
+                    atc_group2 %like% ab_class) %pm>%
+           pm_pull(group) %pm>%
+           unique() %pm>%
+           tolower() %pm>%
+           sort() %pm>% 
+           paste(collapse = "/")
+  )
+}
+
+find_ab_names <- function(ab_group, n = 3) {
+  ab_group <- gsub("[^a-zA-Z|0-9]", ".*", ab_group)
+  
+  # try popular first, they have DDDs
+  drugs <- antibiotics[which((!is.na(antibiotics$iv_ddd) | !is.na(antibiotics$oral_ddd)) &
+                               antibiotics$name %unlike% " " &
+                               antibiotics$group %like% ab_group &
+                               antibiotics$ab %unlike% "[0-9]$"), ]$name
+  if (length(drugs) < n) {
+    # now try it all
+    drugs <- antibiotics[which((antibiotics$group %like% ab_group |
+                                  antibiotics$atc_group1 %like% ab_group |
+                                  antibiotics$atc_group2 %like% ab_group) &
+                                 antibiotics$ab %unlike% "[0-9]$"), ]$name
+  }
+  vector_or(ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
+                    tolower = TRUE,
+                    language = NULL),
+            quotes = FALSE)
 }
--- a/R/ab_from_text.R
+++ b/R/ab_from_text.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,39 +20,40 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Retrieve antimicrobial drug names and doses from clinical text
+#' Retrieve Antimicrobial Drug Names and Doses from Clinical Text
 #' 
 #' Use this function on e.g. clinical texts from health care records. It returns a [list] with all antimicrobial drugs, doses and forms of administration found in the texts.
-#' @inheritSection lifecycle Maturing lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param text text to analyse
 #' @param type type of property to search for, either `"drug"`, `"dose"` or `"administration"`, see *Examples*
-#' @param collapse character to pass on to `paste(..., collapse = ...)` to only return one character per element of `text`, see *Examples*
+#' @param collapse a [character] to pass on to `paste(, collapse = ...)` to only return one [character] per element of `text`, see *Examples*
 #' @param translate_ab if `type = "drug"`: a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]. Defaults to `FALSE`. Using `TRUE` is equal to using "name".
-#' @param thorough_search logical to indicate whether the input must be extensively searched for misspelling and other faulty input values. Setting this to `TRUE` will take considerably more time than when using `FALSE`. At default, it will turn `TRUE` when all input elements contain a maximum of three words.
-#' @param ... parameters passed on to [as.ab()]
-#' @details This function is also internally used by [as.ab()], although it then only searches for the first drug name and will throw a note if more drug names could have been returned.
+#' @param thorough_search a [logical] to indicate whether the input must be extensively searched for misspelling and other faulty input values. Setting this to `TRUE` will take considerably more time than when using `FALSE`. At default, it will turn `TRUE` when all input elements contain a maximum of three words.
+#' @param info a [logical] to indicate whether a progress bar should be printed, defaults to `TRUE` only in interactive mode
+#' @param ... arguments passed on to [as.ab()]
+#' @details This function is also internally used by [as.ab()], although it then only searches for the first drug name and will throw a note if more drug names could have been returned. Note: the [as.ab()] function may use very long regular expression to match brand names of antimicrobial agents. This may fail on some systems.
 #' 
-#' ## Parameter `type`
+#' ## Argument `type`
 #' At default, the function will search for antimicrobial drug names. All text elements will be searched for official names, ATC codes and brand names. As it uses [as.ab()] internally, it will correct for misspelling.
 #' 
-#' With `type = "dose"` (or similar, like "dosing", "doses"), all text elements will be searched for numeric values that are higher than 100 and do not resemble years. The output will be numeric. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, see *Examples*.
+#' With `type = "dose"` (or similar, like "dosing", "doses"), all text elements will be searched for [numeric] values that are higher than 100 and do not resemble years. The output will be [numeric]. It supports any unit (g, mg, IE, etc.) and multiple values in one clinical text, see *Examples*.
 #' 
 #' With `type = "administration"` (or abbreviations, like "admin", "adm"), all text elements will be searched for a form of drug administration. It supports the following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal and vaginal. Abbreviations for oral (such as 'po', 'per os') will become "oral", all values for intravenous (such as 'iv', 'intraven') will become "iv". It supports multiple values in one clinical text, see *Examples*.
 #' 
-#' ## Parameter `collapse`
+#' ## Argument `collapse`
 #' Without using `collapse`, this function will return a [list]. This can be convenient to use e.g. inside a `mutate()`):\cr
 #' `df %>% mutate(abx = ab_from_text(clinical_text))` 
 #' 
-#' The returned AB codes can be transformed to official names, groups, etc. with all [ab_property()] functions like [ab_name()] and [ab_group()], or by using the `translate_ab` parameter.
+#' The returned AB codes can be transformed to official names, groups, etc. with all [`ab_*`][ab_property()] functions such as [ab_name()] and [ab_group()], or by using the `translate_ab` argument.
 #' 
 #' With using `collapse`, this function will return a [character]:\cr
 #' `df %>% mutate(abx = ab_from_text(clinical_text, collapse = "|"))` 
 #' @export
 #' @return A [list], or a  [character] if `collapse` is not `NULL`
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples 
 #' # mind the bad spelling of amoxicillin in this line, 
 #' # straight from a true health care record:
@@ -63,7 +64,7 @@
 #' ab_from_text("500 mg amoxi po and 400mg cipro iv", type = "admin")
 #' 
 #' ab_from_text("500 mg amoxi po and 400mg cipro iv", collapse = ", ")
-#' 
+#' \donttest{
 #' # if you want to know which antibiotic groups were administered, do e.g.:
 #' abx <- ab_from_text("500 mg amoxi po and 400mg cipro iv")
 #' ab_group(abx[[1]])
@@ -86,22 +87,30 @@
 #'                                          collapse = "|"))
 #' 
 #' }
+#' }
 ab_from_text <- function(text,
                         type = c("drug", "dose", "administration"),
                         collapse = NULL,
                         translate_ab = FALSE,
                         thorough_search = NULL,
+                         info = interactive(),
                         ...) {
-  
  if (missing(type)) {
    type <- type[1L]
  }
+  
+  meet_criteria(text)
+  meet_criteria(type, allow_class = "character", has_length = 1)
+  meet_criteria(collapse, has_length = 1, allow_NULL = TRUE)
+  meet_criteria(translate_ab, allow_NULL = FALSE) # get_translate_ab() will be more informative about what's allowed
+  meet_criteria(thorough_search, allow_class = "logical", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(info, allow_class = "logical", has_length = 1)
+
  type <- tolower(trimws(type))
-  stop_if(length(type) != 1, "`type` must be of length 1")
  
  text <- tolower(as.character(text))
  text_split_all <- strsplit(text, "[ ;.,:\\|]")
-  progress <- progress_ticker(n = length(text_split_all), n_min = 5)
+  progress <- progress_ticker(n = length(text_split_all), n_min = 5, print = info)
  on.exit(close(progress))
  
  if (type %like% "(drug|ab|anti)") {
@@ -109,7 +118,7 @@ ab_from_text <- function(text,
    translate_ab <- get_translate_ab(translate_ab)
    
    if (isTRUE(thorough_search) | 
-        (isTRUE(is.null(thorough_search)) & max(sapply(text_split_all, length), na.rm = TRUE) <= 3)) {
+        (isTRUE(is.null(thorough_search)) & max(vapply(FUN.VALUE = double(1), text_split_all, length), na.rm = TRUE) <= 3)) {
      text_split_all <- text_split_all[nchar(text_split_all) >= 4 & grepl("[a-z]+", text_split_all)]
      result <- lapply(text_split_all, function(text_split) {
        progress$tick()
@@ -197,7 +206,7 @@ ab_from_text <- function(text,
  
  # collapse text if needed
  if (!is.null(collapse)) {
-    result <- sapply(result, function(x) {
+    result <- vapply(FUN.VALUE = character(1), result, function(x) {
      if (length(x) == 1 & all(is.na(x))) {
        NA_character_
      } else {
--- a/R/ab_property.R
+++ b/R/ab_property.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,22 +20,22 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Get properties of an antibiotic
+#' Get Properties of an Antibiotic
 #'
 #' 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()].
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param x any (vector of) text that can be coerced to a valid antibiotic 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 tolower a [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 [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 units a [logical] to indicate whether the units instead of the DDDs itself must be returned, see *Examples*
 #' @param open browse the URL using [utils::browseURL()]
-#' @param ... other parameters passed on to [as.ab()]
-#' @details All output will be [translate]d where possible.
+#' @param ... other arguments passed on to [as.ab()]
+#' @details All output [will be translated][translate] where possible.
 #' 
 #' The function [ab_url()] will return the direct URL to the official WHO website. A warning will be returned if the required ATC code is not available.
 #' @inheritSection as.ab Source
@@ -48,8 +48,8 @@
 #' - A [character] in all other cases
 #' @export
 #' @seealso [antibiotics]
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' # all properties:
 #' ab_name("AMX")       # "Amoxicillin"
@@ -89,7 +89,11 @@
 #' ab_atc("cephthriaxone")
 #' ab_atc("seephthriaaksone")
 ab_name <- function(x, language = get_locale(), tolower = FALSE, ...) {
-  x <- translate_AMR(ab_validate(x = x, property = "name", ...), language = language)
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(tolower, allow_class = "logical", has_length = 1)
+  
+  x <- translate_AMR(ab_validate(x = x, property = "name", ...), language = language, only_affect_ab_names = TRUE)
  if (tolower == TRUE) {
    # use perl to only transform the first character
    # as we want "polymyxin B", not "polymyxin b"
@@ -102,18 +106,21 @@ ab_name <- function(x, language = get_locale(), tolower = FALSE, ...) {
 #' @aliases ATC
 #' @export
 ab_atc <- function(x, ...) {
+  meet_criteria(x, allow_NA = TRUE)
  ab_validate(x = x, property = "atc", ...)
 }

 #' @rdname ab_property
 #' @export
 ab_cid <- function(x, ...) {
+  meet_criteria(x, allow_NA = TRUE)
  ab_validate(x = x, property = "cid", ...)
 }

 #' @rdname ab_property
 #' @export
 ab_synonyms <- function(x, ...) {
+  meet_criteria(x, allow_NA = TRUE)
  syns <- ab_validate(x = x, property = "synonyms", ...)
  names(syns) <- x
  if (length(syns) == 1) {
@@ -126,30 +133,38 @@ ab_synonyms <- function(x, ...) {
 #' @rdname ab_property
 #' @export
 ab_tradenames <- function(x, ...) {
+  meet_criteria(x, allow_NA = TRUE)
  ab_synonyms(x, ...)
 }

 #' @rdname ab_property
 #' @export
 ab_group <- function(x, language = get_locale(), ...) {
-  translate_AMR(ab_validate(x = x, property = "group", ...), language = language)
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  translate_AMR(ab_validate(x = x, property = "group", ...), language = language, only_affect_ab_names = TRUE)
 }

 #' @rdname ab_property
 #' @export
 ab_atc_group1 <- function(x, language = get_locale(), ...) {
-  translate_AMR(ab_validate(x = x, property = "atc_group1", ...), language = language)
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  translate_AMR(ab_validate(x = x, property = "atc_group1", ...), language = language, only_affect_ab_names = TRUE)
 }

 #' @rdname ab_property
 #' @export
 ab_atc_group2 <- function(x, language = get_locale(), ...) {
-  translate_AMR(ab_validate(x = x, property = "atc_group2", ...), language = language)
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  translate_AMR(ab_validate(x = x, property = "atc_group2", ...), language = language, only_affect_ab_names = TRUE)
 }

 #' @rdname ab_property
 #' @export
 ab_loinc <- function(x, ...) {
+  meet_criteria(x, allow_NA = TRUE)
  loincs <- ab_validate(x = x, property = "loinc", ...)
  names(loincs) <- x
  if (length(loincs) == 1) {
@@ -162,7 +177,10 @@ ab_loinc <- function(x, ...) {
 #' @rdname ab_property
 #' @export
 ab_ddd <- function(x, administration = "oral", units = FALSE, ...) {
-  stop_ifnot(administration %in% c("oral", "iv"), "`administration` must be 'oral' or 'iv'")
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(administration, is_in = c("oral", "iv"), has_length = 1)
+  meet_criteria(units, allow_class = "logical", has_length = 1)
+
  ddd_prop <- administration
  if (units == TRUE) {
    ddd_prop <- paste0(ddd_prop, "_units")
@@ -175,6 +193,9 @@ ab_ddd <- function(x, administration = "oral", units = FALSE, ...) {
 #' @rdname ab_property
 #' @export
 ab_info <- function(x, language = get_locale(), ...) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- as.ab(x, ...)
  list(ab = as.character(x),
             atc = ab_atc(x),
@@ -194,6 +215,9 @@ ab_info <- function(x, language = get_locale(), ...) {
 #' @rdname ab_property
 #' @export
 ab_url <- function(x, open = FALSE, ...) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(open, allow_class = "logical", has_length = 1)
+  
  ab <- as.ab(x = x, ... = ...)
  u <- paste0("https://www.whocc.no/atc_ddd_index/?code=", ab_atc(ab), "&showdescription=no")
  u[is.na(ab_atc(ab))] <- NA_character_
@@ -201,12 +225,12 @@ ab_url <- function(x, open = FALSE, ...) {
  
  NAs <- ab_name(ab, tolower = TRUE, language = NULL)[!is.na(ab) & is.na(ab_atc(ab))]
  if (length(NAs) > 0) {
-    warning("No ATC code available for ", paste0(NAs, collapse = ", "), ".")
+    warning_("No ATC code available for ", vector_and(NAs, quotes = FALSE), ".")
  }
  
  if (open == TRUE) {
    if (length(u) > 1 & !is.na(u[1L])) {
-      warning("only the first URL will be opened, as `browseURL()` only suports one string.")
+      warning_("Only the first URL will be opened, as `browseURL()` only suports one string.")
    }
    if (!is.na(u[1L])) {
      utils::browseURL(u[1L])
@@ -218,10 +242,9 @@ ab_url <- function(x, open = FALSE, ...) {
 #' @rdname ab_property
 #' @export
 ab_property <- function(x, property = "name", language = get_locale(), ...) {
-  stop_if(length(property) != 1L, "'property' must be of length 1.")
-  stop_ifnot(property %in% colnames(antibiotics),
-             "invalid property: '", property, "' - use a column name of the `antibiotics` data set")
-  
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(property, is_in = colnames(antibiotics), has_length = 1)
+  meet_criteria(language, is_in = c(LANGUAGES_SUPPORTED, ""), has_length = 1, allow_NULL = TRUE, allow_NA = TRUE)
  translate_AMR(ab_validate(x = x, property = property, ...), language = language)
 }

@@ -229,7 +252,7 @@ ab_validate <- function(x, property, ...) {
  
  check_dataset_integrity()
  
-  # try to catch an error when inputting an invalid parameter
+  # try to catch an error when inputting an invalid argument
  # so the 'call.' can be set to FALSE
  tryCatch(x[1L] %in% antibiotics[1, property],
           error = function(e) stop(e$message, call. = FALSE))
@@ -237,10 +260,10 @@ ab_validate <- function(x, property, ...) {
  if (!all(x %in% antibiotics[, property])) {
    x <- data.frame(ab = as.ab(x, ...), stringsAsFactors = FALSE) %pm>%
      pm_left_join(antibiotics, by = "ab") %pm>%
-     pm_pull(property)
+      pm_pull(property)
  }
  if (property == "ab") {
-    return(structure(x, class = property))
+    return(set_clean_class(x, new_class = c("ab", "character")))
  } else if (property == "cid") {
    return(as.integer(x))
  } else if (property %like% "ddd") {
--- a/R/age.R
+++ b/R/age.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,20 +20,24 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Age in years of individuals
+#' Age in Years of Individuals
 #'
 #' Calculates age in years based on a reference date, which is the sytem date at default.
-#' @inheritSection lifecycle Stable lifecycle
-#' @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
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x date(s), [character] (vectors) will be coerced with [as.POSIXlt()]
+#' @param reference reference date(s) (defaults to today), [character] (vectors) will be coerced with [as.POSIXlt()]
+#' @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
+#' @param ... arguments passed on to [as.POSIXlt()], such as `origin`
+#' @details Ages below 0 will be returned as `NA` with a warning. Ages above 120 will only give a warning.
+#' 
+#' This function vectorises over both `x` and `reference`, meaning that either can have a length of 1 while the other argument has a larger length.
 #' @return An [integer] (no decimals) if `exact = FALSE`, a [double] (with decimals) otherwise
 #' @seealso To split ages into groups, use the [age_groups()] function.
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 #' @examples
 #' # 10 random birth dates
@@ -44,13 +48,23 @@
 #' df$age_exact <- age(df$birth_date, exact = TRUE)
 #'
 #' df
-age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) {
+age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
+  meet_criteria(x, allow_class = c("character", "Date", "POSIXt"))
+  meet_criteria(reference, allow_class = c("character", "Date", "POSIXt"))
+  meet_criteria(exact, allow_class = "logical", has_length = 1)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  
  if (length(x) != length(reference)) {
-    stop_if(length(reference) != 1, "`x` and `reference` must be of same length, or `reference` must be of length 1.")
-    reference <- rep(reference, length(x))
+    if (length(x) == 1) {
+      x <- rep(x, length(reference))
+    } else if (length(reference) == 1) {
+      reference <- rep(reference, length(x))
+    } else {
+      stop_("`x` and `reference` must be of same length, or `reference` must be of length 1.")
+    }
  }
-  x <- as.POSIXlt(x)
-  reference <- as.POSIXlt(reference)
+  x <- as.POSIXlt(x, ...)
+  reference <- as.POSIXlt(reference, ...)
  
  # from https://stackoverflow.com/a/25450756/4575331
  years_gap <- reference$year - x$year
@@ -61,54 +75,63 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) {
  # add decimals
  if (exact == TRUE) {
    # get dates of `x` when `x` would have the year of `reference`
-    x_in_reference_year <- as.POSIXlt(paste0(format(reference, "%Y"), format(x, "-%m-%d")))
+    x_in_reference_year <- as.POSIXlt(paste0(format(as.Date(reference), "%Y"),
+                                             format(as.Date(x), "-%m-%d")),
+                                      format = "%Y-%m-%d")
    # get differences in days
-    n_days_x_rest <- as.double(difftime(reference, x_in_reference_year, units = "days"))
+    n_days_x_rest <- as.double(difftime(as.Date(reference), 
+                                        as.Date(x_in_reference_year),
+                                        units = "days"))
    # get numbers of days the years of `reference` has for a reliable denominator
-    n_days_reference_year <- as.POSIXlt(paste0(format(reference, "%Y"), "-12-31"))$yday + 1
+    n_days_reference_year <- as.POSIXlt(paste0(format(as.Date(reference), "%Y"), "-12-31"),
+                                        format = "%Y-%m-%d")$yday + 1
    # add decimal parts of year
    mod <- n_days_x_rest / n_days_reference_year
    # negative mods are cases where `x_in_reference_year` > `reference` - so 'add' a year
-    mod[mod < 0] <- 1 + mod[mod < 0]
+    mod[!is.na(mod) & mod < 0] <- mod[!is.na(mod) & mod < 0] + 1
    # and finally add to ages
    ages <- ages + mod
  }
  
  if (any(ages < 0, na.rm = TRUE)) {
-    ages[ages < 0] <- NA
-    warning("NAs introduced for ages below 0.")
+    ages[!is.na(ages) & ages < 0] <- NA
+    warning_("NAs introduced for ages below 0.", call = TRUE)
  }
  if (any(ages > 120, na.rm = TRUE)) {
-    warning("Some ages are above 120.")
+    warning_("Some ages are above 120.", call = TRUE)
  }
  
  if (isTRUE(na.rm)) {
    ages <- ages[!is.na(ages)]
  }
  
-  ages
+  if (exact == TRUE) {
+    as.double(ages)
+  } else {
+    as.integer(ages)
+  }
 }

-#' Split ages into age groups
+#' Split Ages into Age Groups
 #'
-#' Split ages into age groups defined by the `split` parameter. This allows for easier demographic (antimicrobial resistance) analysis.
-#' @inheritSection lifecycle Stable lifecycle
+#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis.
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 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 for the `split_at` parameter can be:
+#' @details To split ages, the input for the `split_at` argument 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+.
+#' * A [numeric] vector. A value of e.g. `c(10, 20)` will split `x` on 0-9, 10-19 and 20+. A value of only `50` will split `x` 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+.
+#'   - `"fives"`, equivalent of: `1:20 * 5`. This will split on 0-4, 5-9, ..., 95-99, 100+.
+#'   - `"tens"`, equivalent of: `1:10 * 10`. This will split on 0-9, 10-19, ..., 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!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)
 #'
@@ -127,25 +150,28 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE) {
 #' age_groups(ages, split_at = "fives")
 #'
 #' # split specifically for children
-#' age_groups(ages, "children")
-#' # same:
 #' age_groups(ages, c(1, 2, 4, 6, 13, 17))
+#' age_groups(ages, "children")
 #'
 #' \donttest{
-#' # resistance of ciprofloxacine per age group
-#' library(dplyr)
-#' example_isolates %>%
-#'   filter_first_isolate() %>%
-#'   filter(mo == as.mo("E. coli")) %>%
-#'   group_by(age_group = age_groups(age)) %>%
-#'   select(age_group, CIP) %>%
-#'   ggplot_rsi(x = "age_group", minimum = 0)
+#' # resistance of ciprofloxacin per age group
+#' if (require("dplyr")) {
+#'   example_isolates %>%
+#'     filter_first_isolate() %>%
+#'     filter(mo == as.mo("E. coli")) %>%
+#'     group_by(age_group = age_groups(age)) %>%
+#'     select(age_group, CIP) %>%
+#'     ggplot_rsi(x = "age_group", minimum = 0)
+#' }
 #' }
 age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
-  stop_ifnot(is.numeric(x), "`x` must be numeric, not ", paste0(class(x), collapse = "/"))
+  meet_criteria(x, allow_class = c("numeric", "integer"), is_positive_or_zero = TRUE, is_finite = TRUE)
+  meet_criteria(split_at, allow_class = c("numeric", "integer", "character"), is_positive_or_zero = TRUE, is_finite = TRUE)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1) 
+  
  if (any(x < 0, na.rm = TRUE)) {
    x[x < 0] <- NA
-    warning("NAs introduced for ages below 0.")
+    warning_("NAs introduced for ages below 0.", call = TRUE)
  }
  if (is.character(split_at)) {
    split_at <- split_at[1L]
@@ -169,17 +195,17 @@ age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
  
  # turn input values to 'split_at' indices
  y <- x
-  labs <- split_at
+  lbls <- split_at
  for (i in seq_len(length(split_at))) {
    y[x >= split_at[i]] <- i
    # create labels
-    labs[i - 1] <- paste0(unique(c(split_at[i - 1], split_at[i] - 1)), collapse = "-")
+    lbls[i - 1] <- paste0(unique(c(split_at[i - 1], split_at[i] - 1)), collapse = "-")
  }
  
  # last category
-  labs[length(labs)] <- paste0(split_at[length(split_at)], "+")
+  lbls[length(lbls)] <- paste0(split_at[length(split_at)], "+")
  
-  agegroups <- factor(labs[y], levels = labs, ordered = TRUE)
+  agegroups <- factor(lbls[y], levels = lbls, ordered = TRUE)
  
  if (isTRUE(na.rm)) {
    agegroups <- agegroups[!is.na(agegroups)]
--- a/R/amr.R
+++ b/R/amr.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,30 +20,30 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' The `AMR` Package
 #'
 #' Welcome to the `AMR` package.
 #' @details
-#' `AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
+#' `AMR` is a free, open-source and independent \R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
 #' 
-#' After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+#' After installing this package, \R knows `r format_included_data_number(microorganisms)` distinct microbial species and all `r format_included_data_number(rbind(antibiotics[, "atc", drop = FALSE], antivirals[, "atc", drop = FALSE]))` antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
 #' 
-#' This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
+#' This package is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This \R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
 #'
 #' This package can be used for:
 #' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
 #' - Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
 #' - Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
-#' - Determining first isolates to be used for AMR analysis
+#' - Determining first isolates to be used for AMR data 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, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
+#' - Getting properties for any microorganism (such as Gram stain, species, genus or family)
+#' - Getting properties for any antibiotic (such as name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)
 #' - Plotting antimicrobial resistance
 #' - Applying EUCAST expert rules
 #' - Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code
@@ -51,10 +51,10 @@
 #' - Machine reading the EUCAST and CLSI guidelines from 2011-2020 to translate MIC values and disk diffusion diameters to R/SI
 #' - Principal component analysis for AMR
 #' 
-#' @section Reference data publicly available:
+#' @section Reference Data Publicly Available:
 #' All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this `AMR` package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find [all download links on our website](https://msberends.github.io/AMR/articles/datasets.html), which is automatically updated with every code change.
-#' @section Read more on our website!:
-#' On our website <https://msberends.github.io/AMR/> you can find [a comprehensive tutorial](https://msberends.github.io/AMR/articles/AMR.html) about how to conduct AMR analysis, the [complete documentation of all functions](https://msberends.github.io/AMR/reference/) and [an example analysis using WHONET data](https://msberends.github.io/AMR/articles/WHONET.html). As we would like to better understand the backgrounds and needs of our users, please [participate in our survey](https://msberends.github.io/AMR/survey.html)!
+#' @section Read more on Our Website!:
+#' On our website <https://msberends.github.io/AMR/> you can find [a comprehensive tutorial](https://msberends.github.io/AMR/articles/AMR.html) about how to conduct AMR data analysis, the [complete documentation of all functions](https://msberends.github.io/AMR/reference/) and [an example analysis using WHONET data](https://msberends.github.io/AMR/articles/WHONET.html). As we would like to better understand the backgrounds and needs of our users, please [participate in our survey](https://msberends.github.io/AMR/survey.html)!
 #' @section Contact Us:
 #' For suggestions, comments or questions, please contact us at:
 #'
@@ -66,22 +66,10 @@
 #' Post Office Box 30001 \cr
 #' 9700 RB Groningen \cr
 #' The Netherlands
+#' <https://msberends.github.io/AMR/>
 #'
 #' If you have found a bug, please file a new issue at: \cr
 #' <https://github.com/msberends/AMR/issues>
 #' @name AMR
 #' @rdname AMR
 NULL
-
-#' Plotting for classes `rsi` and `disk`
-#' 
-#' Functions to print classes of the `AMR` package.
-#' @inheritSection lifecycle Stable lifecycle
-#' @inheritSection AMR Read more on our website!
-#' @param ... Parameters passed on to functions
-#' @inheritParams base::plot
-#' @inheritParams graphics::barplot
-#' @name plot
-#' @rdname plot
-#' @keywords internal
-NULL
--- a/R/atc_online.R
+++ b/R/atc_online.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,21 +20,21 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Get ATC properties from WHOCC website
+#' Get ATC Properties from WHOCC Website
 #'
 #' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic), such as the name, defined daily dose (DDD) or standard unit.
-#' @inheritSection lifecycle Stable lifecycle
-#' @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 `"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
+#' @inheritSection lifecycle Stable Lifecycle
+#' @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 `"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 WHOCC. The sign `%s` can be used as a placeholder for ATC codes.
 #' @param url_vet url of website of the WHOCC for veterinary medicine. The sign `%s` can be used as a placeholder for ATC_vet codes (that all start with "Q").
-#' @param ... parameters to pass on to `atc_property`
+#' @param ... arguments to pass on to `atc_property`
 #' @details
-#' Options for parameter `administration`:
+#' Options for argument `administration`:
 #' 
 #' - `"Implant"` = Implant
 #' - `"Inhal"` = Inhalation
@@ -56,28 +56,35 @@
 #' - `"TU"` = thousand units
 #' - `"MU"` = million units
 #' - `"mmol"` = millimole
-#' - `"ml"` = milliliter (e.g. eyedrops)
+#' - `"ml"` = millilitre (e.g. eyedrops)
 #' 
 #' **N.B. This function requires an internet connection and only works if the following packages are installed: `curl`, `rvest`, `xml2`.**
 #' @export
 #' @rdname atc_online
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @source <https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/>
 #' @examples
 #' \donttest{
-#' # oral DDD (Defined Daily Dose) of amoxicillin
-#' atc_online_property("J01CA04", "DDD", "O")
+#' if (requireNamespace("curl") && requireNamespace("rvest") && requireNamespace("xml2")) { 
+#'   # oral DDD (Defined Daily Dose) of amoxicillin
+#'   atc_online_property("J01CA04", "DDD", "O")
 #' 
-#' # parenteral DDD (Defined Daily Dose) of amoxicillin
-#' atc_online_property("J01CA04", "DDD", "P")
+#'   # parenteral DDD (Defined Daily Dose) of amoxicillin
+#'   atc_online_property("J01CA04", "DDD", "P")
 #'
-#' atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
+#'   atc_online_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
+#' }
 #' }
 atc_online_property <- function(atc_code,
                                property,
                                administration = "O",
                                url = "https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no",
                                url_vet = "https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no") {
+  meet_criteria(atc_code, allow_class = "character")
+  meet_criteria(property, allow_class = "character", has_length = 1, is_in = c("ATC", "Name", "DDD", "U", "Adm.R", "Note", "groups"), ignore.case = TRUE)
+  meet_criteria(administration, allow_class = "character", has_length = 1)
+  meet_criteria(url, allow_class = "character", has_length = 1, looks_like = "https?://")
+  meet_criteria(url_vet, allow_class = "character", has_length = 1, looks_like = "https?://")
  
  has_internet <- import_fn("has_internet", "curl")
  html_attr <- import_fn("html_attr", "rvest")
@@ -95,28 +102,18 @@ atc_online_property <- function(atc_code,
  }
  
  if (!has_internet()) {
-    message("There appears to be no internet connection, returning NA.")
+    message_("There appears to be no internet connection, returning NA.",
+             add_fn = font_red,
+             as_note = FALSE)
    return(rep(NA, length(atc_code)))
  }
  
-  stop_if(length(property) != 1L, "`property` must be of length 1")
-  stop_if(length(administration) != 1L, "`administration` must be of length 1")
-  
  # also allow unit as property
  if (property %like% "unit") {
    property <- "U"
  }
  
-  # validation of properties
-  valid_properties <- c("ATC", "Name", "DDD", "U", "Adm.R", "Note", "groups")
-  valid_properties.bak <- valid_properties
-  
  property <- tolower(property)
-  valid_properties <- tolower(valid_properties)
-  
-  stop_ifnot(property %in% valid_properties,
-             "Invalid `property`, use one of ", paste(valid_properties.bak, collapse = ", "))
-  
  if (property == "ddd") {
    returnvalue <- rep(NA_real_, length(atc_code))
  } else if (property == "groups") {
@@ -166,7 +163,7 @@ atc_online_property <- function(atc_code,
      colnames(tbl) <- gsub("^atc.*", "atc", tolower(colnames(tbl)))
      
      if (length(tbl) == 0) {
-        warning("ATC not found: ", atc_code[i], ". Please check ", atc_url, ".", call. = FALSE)
+        warning_("ATC not found: ", atc_code[i], ". Please check ", atc_url, ".", call = FALSE)
        returnvalue[i] <- NA
        next
      }
@@ -199,11 +196,13 @@ atc_online_property <- function(atc_code,
 #' @rdname atc_online
 #' @export
 atc_online_groups <- function(atc_code, ...) {
+  meet_criteria(atc_code, allow_class = "character")
  atc_online_property(atc_code = atc_code, property = "groups", ...)
 }

 #' @rdname atc_online
 #' @export
 atc_online_ddd <- function(atc_code, ...) {
+  meet_criteria(atc_code, allow_class = "character")
  atc_online_property(atc_code = atc_code, property = "ddd", ...)
 }
--- a/R/availability.R
+++ b/R/availability.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,35 +20,38 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Check availability of columns
+#' Check Availability of Columns
 #'
 #' Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g. [susceptibility()] and [resistance()].
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 [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!
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 #' @examples
 #' availability(example_isolates)
-#'
+#' \donttest{
 #' if (require("dplyr")) {
 #'   example_isolates %>%
 #'     filter(mo == as.mo("E. coli")) %>%
 #'     select_if(is.rsi) %>%
 #'     availability()
 #' }
+#' }
 availability <- function(tbl, width = NULL) {
-  stop_ifnot(is.data.frame(tbl), "`tbl` must be a data.frame")
-  x <- sapply(tbl, function(x) {
+  meet_criteria(tbl, allow_class = "data.frame")
+  meet_criteria(width, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
+  
+  x <- vapply(FUN.VALUE = double(1), tbl, function(x) {
    1 - sum(is.na(x)) / length(x) 
  })
-  n <- sapply(tbl, function(x) length(x[!is.na(x)]))
-  R <- sapply(tbl, function(x) ifelse(is.rsi(x), resistance(x, minimum = 0), NA))
+  n <- vapply(FUN.VALUE = double(1), tbl, function(x) length(x[!is.na(x)]))
+  R <- vapply(FUN.VALUE = double(1), tbl, function(x) ifelse(is.rsi(x), resistance(x, minimum = 0), NA_real_))
  R_print <- character(length(R))
  R_print[!is.na(R)] <- percentage(R[!is.na(R)])
  R_print[is.na(R)] <- ""
@@ -83,7 +86,8 @@ availability <- function(tbl, width = NULL) {
                   available = percentage(x),
                   visual_availabilty = paste0("|", x_chars, x_chars_empty, "|"),
                   resistant = R_print,
-                   visual_resistance = vis_resistance)
+                   visual_resistance = vis_resistance,
+                   stringsAsFactors = FALSE)
  if (length(R[is.na(R)]) == ncol(tbl)) {
    df[, 1:3]
  } else {
--- a/R/bug_drug_combinations.R
+++ b/R/bug_drug_combinations.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,19 +20,19 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Determine bug-drug combinations
+#' 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 [format()] on the result to prettify it to a publicable/printable format, see Examples.
-#' @inheritSection lifecycle Stable lifecycle
+#' 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*.
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 and columns with 100% resistance for all tested antimicrobials must be removed from the table
+#' @param combine_IR a [logical] to indicate whether values R and I should be summed
+#' @param add_ab_group a [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 and columns 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 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
@@ -41,7 +41,7 @@
 #' @rdname bug_drug_combinations
 #' @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)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples 
 #' \donttest{
 #' x <- bug_drug_combinations(example_isolates)
@@ -61,34 +61,37 @@ bug_drug_combinations <- function(x,
                                  col_mo = NULL, 
                                  FUN = mo_shortname,
                                  ...) {
-  stop_ifnot(is.data.frame(x), "`x` must be a data frame")
-  stop_ifnot(any(sapply(x, is.rsi), na.rm = TRUE), "No columns with class <rsi> found. See ?as.rsi.")
-  
+  meet_criteria(x, allow_class = "data.frame", contains_column_class = "rsi")
+  meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE)
+  meet_criteria(FUN, allow_class = "function", has_length = 1)
+
  # try to find columns based on type
  # -- mo
  if (is.null(col_mo)) {
    col_mo <- search_type_in_df(x = x, type = "mo")
+    stop_if(is.null(col_mo), "`col_mo` must be set")
+  } else {
+    stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found")
  }
-  stop_if(is.null(col_mo), "`col_mo` must be set")
  
  x_class <- class(x)
  x <- as.data.frame(x, stringsAsFactors = FALSE)
  x[, col_mo] <- FUN(x[, col_mo, drop = TRUE], ...)
-  x <- x[, c(col_mo, names(which(sapply(x, is.rsi)))), drop = FALSE]
+  x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.rsi)))), drop = FALSE]
  
  unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
  
-  out <- data.frame(
-    mo = character(0),
-    ab = character(0),
-    S = integer(0),
-    I = integer(0),
-    R = integer(0),
-    total = integer(0))
+  out <- data.frame(mo = character(0),
+                    ab = character(0),
+                    S = integer(0),
+                    I = integer(0),
+                    R = integer(0),
+                    total = integer(0),
+                    stringsAsFactors = FALSE)
  
  for (i in seq_len(length(unique_mo))) {
    # filter on MO group and only select R/SI columns
-    x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(sapply(x, is.rsi))), drop = FALSE]
+    x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.rsi))), drop = FALSE]
    # turn and merge everything
    pivot <- lapply(x_mo_filter, function(x) {
      m <- as.matrix(table(x))
@@ -100,11 +103,13 @@ bug_drug_combinations <- function(x,
                            S = merged$S,
                            I = merged$I,
                            R = merged$R,
-                            total = merged$S + merged$I + merged$R)
-    out <- rbind(out, out_group)
+                            total = merged$S + merged$I + merged$R,
+                            stringsAsFactors = FALSE)
+    out <- rbind(out, out_group, stringsAsFactors = FALSE)
  }
  
-  structure(.Data = out, class = c("bug_drug_combinations", x_class))
+  set_clean_class(out,
+                  new_class = c("bug_drug_combinations", x_class))
 }

 #' @method format bug_drug_combinations
@@ -121,6 +126,17 @@ format.bug_drug_combinations <- function(x,
                                         decimal.mark = getOption("OutDec"),
                                         big.mark = ifelse(decimal.mark == ",", ".", ","),
                                         ...) {
+  meet_criteria(x, allow_class = "data.frame")
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
+  meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
+  meet_criteria(add_ab_group, allow_class = "logical", has_length = 1)
+  meet_criteria(remove_intrinsic_resistant, allow_class = "logical", has_length = 1)
+  meet_criteria(decimal.mark, allow_class = "character", has_length = 1)
+  meet_criteria(big.mark, allow_class = "character", has_length = 1)
+  
  x <- as.data.frame(x, stringsAsFactors = FALSE)
  x <- subset(x, total >= minimum)
  
@@ -151,7 +167,8 @@ format.bug_drug_combinations <- function(x,
  
  remove_NAs <- function(.data) {
    cols <- colnames(.data)
-    .data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE))
+    .data <- as.data.frame(lapply(.data, function(x) ifelse(is.na(x), "", x)),
+                           stringsAsFactors = FALSE)
    colnames(.data) <- cols
    .data
  }
@@ -220,7 +237,7 @@ format.bug_drug_combinations <- function(x,
  }
  
  if (remove_intrinsic_resistant == TRUE) {
-    y <- y[, !sapply(y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
+    y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
  }
  
  rownames(y) <- NULL
@@ -231,7 +248,8 @@ format.bug_drug_combinations <- function(x,
 #' @export
 print.bug_drug_combinations <- function(x, ...) {
  x_class <- class(x)
-  print(structure(x, class = x_class[x_class != "bug_drug_combinations"]),
+  print(set_clean_class(x, 
+                        new_class = x_class[x_class != "bug_drug_combinations"]),
        ...)
-  message(font_blue("NOTE: Use 'format()' on this result to get a publishable/printable format."))
+  message_("Use 'format()' on this result to get a publishable/printable format.", as_note = FALSE)
 }
--- a/R/catalogue_of_life.R
+++ b/R/catalogue_of_life.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,30 +20,46 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

+format_included_data_number <- function(data) {
+  if (is.data.frame(data)) {
+    n <- nrow(data)
+  } else {
+    n <- length(unique(data))
+  }
+  if (n > 10000) {
+    rounder <- -3 # round on thousands
+  } else if (n > 1000) {
+    rounder <- -2 # round on hundreds
+  } else {
+    rounder <- -1 # round on tens
+  }
+  paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = ","))
+}
+
 #' The Catalogue of Life
 #'
 #' 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 (<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 (`r format_included_data_number(microorganisms)` species) from the authoritative and comprehensive Catalogue of Life (CoL, <http://www.catalogueoflife.org>). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, [lpsn.dsmz.de](https://lpsn.dsmz.de)). This supplementation is needed until the [CoL+ project](https://github.com/CatalogueOfLife/general) is finished, which we await.
 #'
-#' [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:
+#' [Click here][catalogue_of_life] for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with [catalogue_of_life_version()].
+#' @section Included Taxa:
 #' Included are:
-#' - 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)
+#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria", "Chromista", "Protozoa")), ])` (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa
+#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi" & microorganisms$order %in% c("Eurotiales", "Microascales", "Mucorales", "Onygenales", "Pneumocystales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales")), ])` (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales, as well as `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi" & !microorganisms$order %in% c("Eurotiales", "Microascales", "Mucorales", "Onygenales", "Pneumocystales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales")), ])` other fungal (sub)species. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
+#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), ])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus"])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
+#' - All `r format_included_data_number(microorganisms.old)` 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 (<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 syntax used to transform the original data to a cleansed R format, can be found here: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
-#' @inheritSection AMR Read more on our website!
+#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/master/data-raw/reproduction_of_microorganisms.R>.
+#' @inheritSection AMR Read more on Our Website!
 #' @name catalogue_of_life
 #' @rdname catalogue_of_life
 #' @seealso Data set [microorganisms] for the actual data. \cr
@@ -57,74 +73,73 @@
 #' mo_shortname("Chlamydophila psittaci")
 #' # Note: 'Chlamydophila psittaci' (Everett et al., 1999) was renamed back to
 #' #       'Chlamydia psittaci' (Page, 1968)
-#' # [1] "C. psittaci"
+#' #> [1] "C. psittaci"
 #'
 #' # Get any property from the entire taxonomic tree for all included species
 #' mo_class("E. coli")
-#' # [1] "Gammaproteobacteria"
+#' #> [1] "Gammaproteobacteria"
 #'
 #' mo_family("E. coli")
-#' # [1] "Enterobacteriaceae"
+#' #> [1] "Enterobacteriaceae"
 #'
 #' mo_gramstain("E. coli") # based on kingdom and phylum, see ?mo_gramstain
-#' # [1] "Gram negative"
+#' #> [1] "Gram-negative"
 #'
 #' mo_ref("E. coli")
-#' # [1] "Castellani et al., 1919"
+#' #> [1] "Castellani et al., 1919"
 #'
 #' # Do not get mistaken - this package is about microorganisms
 #' mo_kingdom("C. elegans")
-#' # [1] "Fungi"                 # Fungi?!
+#' #> [1] "Fungi"                 # Fungi?!
 #' mo_name("C. elegans")
-#' # [1] "Cladosporium elegans"  # Because a microorganism was found
+#' #> [1] "Cladosporium elegans"  # Because a microorganism was found
 NULL

 #' Version info of included Catalogue of Life
 #'
 #' This function returns information about the included data from the Catalogue of Life.
 #' @seealso [microorganisms]
-#' @details For DSMZ, see [microorganisms].
+#' @details For LPSN, see [microorganisms].
 #' @return a [list], which prints in pretty format
 #' @inheritSection catalogue_of_life Catalogue of Life
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 catalogue_of_life_version <- function() {
  
  check_dataset_integrity()
  
-  # see the `catalogue_of_life` list in R/data.R
-  lst <- list(catalogue_of_life =
-                list(version = gsub("{year}", catalogue_of_life$year, catalogue_of_life$version, fixed = TRUE),
-                     url = gsub("{year}", catalogue_of_life$year, catalogue_of_life$url_CoL, fixed = TRUE),
+  # see the `CATALOGUE_OF_LIFE` list in R/globals.R
+  lst <- list(CoL =
+                list(version = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE),
+                     url = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$url_CoL, fixed = TRUE),
                     n = nrow(pm_filter(microorganisms, source == "CoL"))),
-              deutsche_sammlung_von_mikroorganismen_und_zellkulturen =
-                list(version = "Prokaryotic Nomenclature Up-to-Date from DSMZ",
-                     url = catalogue_of_life$url_DSMZ,
-                     yearmonth = catalogue_of_life$yearmonth_DSMZ,
-                     n = nrow(pm_filter(microorganisms, source == "DSMZ"))),
+              LPSN =
+                list(version = "List of Prokaryotic names with Standing in Nomenclature",
+                     url = CATALOGUE_OF_LIFE$url_LPSN,
+                     yearmonth = CATALOGUE_OF_LIFE$yearmonth_LPSN,
+                     n = nrow(pm_filter(microorganisms, source == "LPSN"))),
              total_included =
                list(
                  n_total_species = nrow(microorganisms),
                  n_total_synonyms = nrow(microorganisms.old)))
  
-  structure(.Data = lst,
-            class = c("catalogue_of_life_version", "list"))
+  set_clean_class(lst,
+                  new_class = c("catalogue_of_life_version", "list"))
 }

 #' @method print catalogue_of_life_version
 #' @export
 #' @noRd
 print.catalogue_of_life_version <- function(x, ...) {
-  lst <- x
-  cat(paste0(font_bold("Included in this AMR package are:\n\n"),
-             font_underline(lst$catalogue_of_life$version), "\n",
-             "  Available at: ", lst$catalogue_of_life$url, "\n",
-             "  Number of included species: ", format(lst$catalogue_of_life$n, big.mark = ","), "\n",
-             font_underline(paste0(lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$version, " (",
-                                   lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$yearmonth, ")")), "\n",
-             "  Available at: ", lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$url, "\n",
-             "  Number of included species: ", format(lst$deutsche_sammlung_von_mikroorganismen_und_zellkulturen$n, big.mark = ","), "\n\n",
-             "=> Total number of species included:  ", format(lst$total_included$n_total_species, big.mark = ","), "\n",
-             "=> Total number of synonyms included: ", format(lst$total_included$n_total_synonyms, big.mark = ","), "\n\n",
+  cat(paste0(font_bold("Included in this AMR package (v", utils::packageDescription("AMR")$Version, ") are:\n\n", collapse = ""),
+             font_underline(x$CoL$version), "\n",
+             "  Available at: ", font_blue(x$CoL$url), "\n",
+             "  Number of included microbial species: ", format(x$CoL$n, big.mark = ","), "\n",
+             font_underline(paste0(x$LPSN$version, " (",
+                                   x$LPSN$yearmonth, ")")), "\n",
+             "  Available at: ", font_blue(x$LPSN$url), "\n",
+             "  Number of included bacterial species: ", format(x$LPSN$n, big.mark = ","), "\n\n",
+             "=> Total number of species included:  ", format(x$total_included$n_total_species, big.mark = ","), "\n",
+             "=> Total number of synonyms included: ", format(x$total_included$n_total_synonyms, big.mark = ","), "\n\n",
             "See for more info ?microorganisms and ?catalogue_of_life.\n"))
 }
--- a/R/count.R
+++ b/R/count.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,15 +20,15 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Count available 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 `summarise()` from the `dplyr` package and also support grouped variables, please see *Examples*.
+#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
 #'
 #' [count_resistant()] should be used to count resistant isolates, [count_susceptible()] should be used to count susceptible isolates.
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 R and S/I
@@ -39,13 +39,13 @@
 #' 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. It also supports grouped variables. The function [rsi_df()] works exactly like [count_df()], but adds the percentage of S, I and R.
-#' @inheritSection proportion Combination therapy
+#' @inheritSection proportion Combination Therapy
 #' @seealso [`proportion_*`][proportion] to calculate microbial resistance and susceptibility.
 #' @return An [integer]
 #' @rdname count
 #' @name count
 #' @export
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' # example_isolates is a data set available in the AMR package.
 #' ?example_isolates
@@ -72,7 +72,7 @@
 #' count_susceptible(example_isolates$AMX)
 #' susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
 #'
-#' 
+#' \donttest{
 #' if (require("dplyr")) {
 #'   example_isolates %>%
 #'     group_by(hospital_id) %>%
@@ -106,6 +106,7 @@
 #'     group_by(hospital_id) %>%
 #'     count_df(translate = FALSE)
 #' }
+#' }
 count_resistant <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = "R",
@@ -134,7 +135,10 @@ count_R <- function(..., only_all_tested = FALSE) {
 #' @rdname count
 #' @export
 count_IR <- function(..., only_all_tested = FALSE) {
-  warning("Using 'count_IR' is discouraged; use 'count_resistant()' instead to not consider \"I\" being resistant.", call. = FALSE)
+  if (message_not_thrown_before("count_IR")) {
+    warning_("Using count_IR() is discouraged; use count_resistant() instead to not consider \"I\" being resistant.", call = FALSE)
+    remember_thrown_message("count_IR")
+  }
  rsi_calc(...,
           ab_result = c("I", "R"),
           only_all_tested = only_all_tested,
@@ -162,7 +166,10 @@ count_SI <- function(..., only_all_tested = FALSE) {
 #' @rdname count
 #' @export
 count_S <- function(..., only_all_tested = FALSE) {
-  warning("Using 'count_S' is discouraged; use 'count_susceptible()' instead to also consider \"I\" being susceptible.", call. = FALSE)
+  if (message_not_thrown_before("count_S")) {
+    warning_("Using count_S() is discouraged; use count_susceptible() instead to also consider \"I\" being susceptible.", call = FALSE)
+    remember_thrown_message("count_S")
+  }
  rsi_calc(...,
           ab_result = "S",
           only_all_tested = only_all_tested,
@@ -189,7 +196,6 @@ count_df <- function(data,
                     language = get_locale(),
                     combine_SI = TRUE,
                     combine_IR = FALSE) {
-  
  rsi_calc_df(type = "count",
              data = data,
              translate_ab = translate_ab,
--- a/R/custom_eucast_rules.R
+++ b/R/custom_eucast_rules.R
@@ -0,0 +1,255 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       #
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' Define Custom EUCAST Rules
+#' 
+#' Define custom EUCAST rules for your organisation or specific analysis and use the output of this function in [eucast_rules()].
+#' @inheritSection lifecycle Maturing Lifecycle
+#' @param ... rules in formula notation, see *Examples*
+#' @details
+#' Some organisations have their own adoption of EUCAST rules. This function can be used to define custom EUCAST rules to be used in the [eucast_rules()] function.
+#' 
+#' @section How it works:
+#' 
+#' ### Basics
+#' 
+#' If you are familiar with the [`case_when()`][dplyr::case_when()] function of the `dplyr` package, you will recognise the input method to set your own rules. Rules must be set using what \R considers to be the 'formula notation'. The rule itself is written *before* the tilde (`~`) and the consequence of the rule is written *after* the tilde:
+#' 
+#' ```
+#' x <- custom_eucast_rules(TZP == "S" ~ aminopenicillins == "S",
+#'                          TZP == "R" ~ aminopenicillins == "R")
+#' ```
+#' 
+#' These are two custom EUCAST rules: if TZP (piperacillin/tazobactam) is "S", all aminopenicillins (ampicillin and amoxicillin) must be made "S", and if TZP is "R", aminopenicillins must be made "R". These rules can also be printed to the console, so it is immediately clear how they work:
+#' 
+#' ```
+#' x
+#' #> A set of custom EUCAST rules:
+#' #> 
+#' #>   1. If TZP is S then set to S:
+#' #>      amoxicillin (AMX), ampicillin (AMP)
+#' #> 
+#' #>   2. If TZP is R then set to R:
+#' #>      amoxicillin (AMX), ampicillin (AMP)
+#' ```
+#' 
+#' The rules (the part *before* the tilde, in above example `TZP == "S"` and `TZP == "R"`) must be evaluable in your data set: it should be able to run as a filter in your data set without errors. This means for the above example that the column `TZP` must exist. We will create a sample data set and test the rules set:
+#' 
+#' ```
+#' df <- data.frame(mo = c("E. coli", "K. pneumoniae"),
+#'                  TZP = "R",
+#'                  amox = "",
+#'                  AMP = "")
+#' df
+#' #>              mo TZP amox AMP
+#' #> 1       E. coli   R         
+#' #> 2 K. pneumoniae   R         
+#'                  
+#' eucast_rules(df, rules = "custom", custom_rules = x)
+#' #>              mo TZP amox AMP
+#' #> 1       E. coli   R    R   R     
+#' #> 2 K. pneumoniae   R    R   R  
+#' ```
+#' 
+#' ### Using taxonomic properties in rules
+#' 
+#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), quote = "``", sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
+#' 
+#' ```
+#' y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
+#'                          TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")
+#'
+#' eucast_rules(df, rules = "custom", custom_rules = y)
+#' #>              mo TZP amox AMP
+#' #> 1       E. coli   R         
+#' #> 2 K. pneumoniae   R    R   R
+#' ```
+#' 
+#' ### Usage of antibiotic group names
+#' 
+#' It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part *after* the tilde. In above examples, the antibiotic group `aminopenicillins` is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the antibiotic agents that will be matched when running the rule.
+#' 
+#' `r paste0("  * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("``", tolower(x), "``\\cr(", paste0(sort(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE)), collapse = ", "), ")"), USE.NAMES = FALSE), "\n", collapse = "")`
+#' @returns A [list] containing the custom rules
+#' @inheritSection AMR Read more on Our Website!
+#' @export
+#' @examples
+#' x <- custom_eucast_rules(AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",
+#'                          AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")
+#' eucast_rules(example_isolates,
+#'              rules = "custom",
+#'              custom_rules = x,
+#'              info = FALSE)
+#'              
+#' # combine rule sets
+#' x2 <- c(x,
+#'         custom_eucast_rules(TZP == "R" ~ carbapenems == "R"))
+#' x2
+custom_eucast_rules <- function(...) {
+  
+  dots <- tryCatch(list(...),
+                   error = function(e) "error")
+  stop_if(identical(dots, "error"),
+          "rules must be a valid formula inputs (e.g., using '~'), see `?custom_eucast_rules`")
+  n_dots <- length(dots)
+  stop_if(n_dots == 0, "no custom rules were set. Please read the documentation using `?custom_eucast_rules`.")
+  out <- vector("list", n_dots)
+  for (i in seq_len(n_dots)) {
+    stop_ifnot(inherits(dots[[i]], "formula"), 
+               "rule ", i, " must be a valid formula input (e.g., using '~'), see `?custom_eucast_rules`")
+    
+    # Query
+    qry <- dots[[i]][[2]]
+    if (inherits(qry, "call")) {
+      qry <- as.expression(qry)
+    }
+    qry <- as.character(qry)
+    # these will prevent vectorisation, so replace them:
+    qry <- gsub("&&", "&", qry, fixed = TRUE)
+    qry <- gsub("||", "|", qry, fixed = TRUE)
+    # format nicely, setting spaces around operators
+    qry <- gsub(" *([&|+-/*^><==]+) *", " \\1 ", qry)
+    qry <- gsub(" ?, ?", ", ", qry)
+    qry <- gsub("'", "\"", qry, fixed = TRUE)
+    out[[i]]$query <- as.expression(qry)
+    
+    # Resulting rule
+    result <- dots[[i]][[3]]
+    stop_ifnot(deparse(result) %like% "==", 
+               "the result of rule ", i, " (the part after the `~`) must contain `==`, such as in `... ~ ampicillin == \"R\"`, see `?custom_eucast_rules`")
+    result_group <- as.character(result)[[2]]
+    if (paste0(toupper(result_group), "S") %in% DEFINED_AB_GROUPS) {
+      # support for e.g. 'aminopenicillin' if user meant 'aminopenicillins'
+      result_group <- paste0(result_group, "s")
+    }
+    if (toupper(result_group) %in% DEFINED_AB_GROUPS) {
+      result_group <- eval(parse(text = toupper(result_group)), envir = asNamespace("AMR"))
+    } else {
+      result_group <- tryCatch(
+        suppressWarnings(as.ab(result_group,
+                               fast_mode = TRUE,
+                               flag_multiple_results = FALSE)),
+        error = function(e) NA_character_)
+    }
+    
+    stop_if(any(is.na(result_group)),
+            "this result of rule ", i, " could not be translated to a single antimicrobial agent/group: \"", 
+            as.character(result)[[2]], "\".\n\nThe input can be a name or code of an antimicrobial agent, or be one of: ", 
+            vector_or(tolower(DEFINED_AB_GROUPS), quotes = FALSE), ".")
+    result_value <- as.character(result)[[3]]
+    result_value[result_value == "NA"] <- NA
+    stop_ifnot(result_value %in% c("R", "S", "I", NA),
+               "the resulting value of rule ", i, " must be either \"R\", \"S\", \"I\" or NA")
+    result_value <- as.rsi(result_value)
+    
+    out[[i]]$result_group <- result_group
+    out[[i]]$result_value <- result_value
+  }
+  
+  names(out) <- paste0("rule", seq_len(n_dots))
+  set_clean_class(out, new_class = c("custom_eucast_rules", "list"))
+}
+
+#' @method c custom_eucast_rules
+#' @noRd
+#' @export
+c.custom_eucast_rules <- function(x, ...) {
+  if (length(list(...)) == 0) {
+    return(x)
+  }
+  out <- unclass(x)
+  for (e in list(...)) {
+    out <- c(out, unclass(e))
+  }
+  names(out) <- paste0("rule", seq_len(length(out)))
+  set_clean_class(out, new_class = c("custom_eucast_rules", "list"))
+}
+
+#' @method as.list custom_eucast_rules
+#' @noRd
+#' @export
+as.list.custom_eucast_rules <- function(x, ...) {
+  c(x, ...)
+}
+
+#' @method print custom_eucast_rules
+#' @export
+#' @noRd
+print.custom_eucast_rules <- function(x, ...) {
+  cat("A set of custom EUCAST rules:\n")
+  for (i in seq_len(length(x))) {
+    rule <- x[[i]]
+    rule$query <- format_custom_query_rule(rule$query)
+    if (is.na(rule$result_value)) {
+      val <- font_red("<NA>")
+    } else if (rule$result_value == "R") {
+      val <- font_rsi_R_bg(font_black(" R "))
+    } else if (rule$result_value == "S") {
+      val <- font_rsi_S_bg(font_black(" S "))
+    } else {
+      val <- font_rsi_I_bg(font_black(" I "))
+    }
+    agents <- paste0(font_blue(ab_name(rule$result_group, language = NULL, tolower = TRUE),
+                               collapse = NULL),
+                     " (", rule$result_group, ")")
+    agents <- sort(agents)
+    rule_if <- word_wrap(paste0(i, ". ", font_bold("If "), font_blue(rule$query), font_bold(" then "),
+                            "set to {result}:"),
+                     extra_indent = 5)
+    rule_if <- gsub("{result}", val, rule_if, fixed = TRUE)
+    rule_then <- paste0("     ", word_wrap(paste0(agents, collapse = ", "), extra_indent = 5))
+    cat("\n  ", rule_if, "\n", rule_then, "\n", sep = "")
+  }
+}
+
+format_custom_query_rule <- function(query, colours = has_colour()) {
+  query <- gsub(" & ", font_black(font_bold(" and ")), query, fixed = TRUE)
+  query <- gsub(" | ", font_black(" or "), query, fixed = TRUE)
+  query <- gsub(" + ", font_black(" plus "), query, fixed = TRUE)
+  query <- gsub(" - ", font_black(" minus "), query, fixed = TRUE)
+  query <- gsub(" / ", font_black(" divided by "), query, fixed = TRUE)
+  query <- gsub(" * ", font_black(" times "), query, fixed = TRUE)
+  query <- gsub(" == ", font_black(" is "), query, fixed = TRUE)
+  query <- gsub(" > ", font_black(" is higher than "), query, fixed = TRUE)
+  query <- gsub(" < ", font_black(" is lower than "), query, fixed = TRUE)
+  query <- gsub(" >= ", font_black(" is higher than or equal to "), query, fixed = TRUE)
+  query <- gsub(" <= ", font_black(" is lower than or equal to "), query, fixed = TRUE)
+  query <- gsub(" ^ ", font_black(" to the power of "), query, fixed = TRUE)
+  query <- gsub(" %in% ", font_black(" is one of "), query, fixed = TRUE)
+  query <- gsub(" %like% ", font_black(" resembles "), query, fixed = TRUE)
+  if (colours == TRUE) {
+    query <- gsub('"R"', font_rsi_R_bg(font_black(" R ")), query, fixed = TRUE)
+    query <- gsub('"S"', font_rsi_S_bg(font_black(" S ")), query, fixed = TRUE)
+    query <- gsub('"I"', font_rsi_I_bg(font_black(" I ")), query, fixed = TRUE)
+  }
+  # replace the black colour 'stops' with blue colour 'starts'
+  query <- gsub("\033[39m", "\033[34m", as.character(query), fixed = TRUE)
+  # start with blue
+  query <- paste0("\033[34m", query)
+  if (colours == FALSE) {
+    query <- font_stripstyle(query)
+  }
+  query
+}
--- a/R/data.R
+++ b/R/data.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,15 +20,15 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Data sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = ",")` antimicrobials
+#' Data Sets with `r format(nrow(antibiotics) + nrow(antivirals), big.mark = ",")` Antimicrobials
 #'
-#' 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_*`][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
-#' ### For the [antibiotics] data set: a [data.frame] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` 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
+#' ## For the [antibiotics] data set: a [data.frame] with `r nrow(antibiotics)` observations and `r ncol(antibiotics)` variables:
+#' - `ab`\cr Antibiotic ID as used in this package (such as `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
@@ -43,7 +43,7 @@
 #' - `iv_units`\cr Units of `iv_ddd`
 #' - `loinc`\cr All LOINC codes (Logical Observation Identifiers Names and Codes) associated with the name of the antimicrobial agent. Use [ab_loinc()] to retrieve them quickly, see [ab_property()].
 #' 
-#' ### For the [antivirals] data set: a [data.frame] with `r nrow(antivirals)` observations and `r ncol(antivirals)` variables:
+#' ## For the [antivirals] data set: a [data.frame] with `r nrow(antivirals)` observations and `r ncol(antivirals)` 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
@@ -57,13 +57,13 @@
 #'
 #' Synonyms (i.e. trade names) are derived from the Compound ID (`cid`) and consequently only available where a CID is available.
 #' 
-#' ### Direct download
-#' These data sets are available as 'flat files' for use even without R - you can find the files here:
+#' ## Direct download
+#' These data sets are available as 'flat files' for use even without \R - you can find the files here:
 #' 
 #' * <https://github.com/msberends/AMR/raw/master/data-raw/antibiotics.txt>
 #' * <https://github.com/msberends/AMR/raw/master/data-raw/antivirals.txt>
 #' 
-#' Files in R format (with preserved data structure) can be found here:
+#' Files in \R format (with preserved data structure) can be found here:
 #' 
 #' * <https://github.com/msberends/AMR/raw/master/data/antibiotics.rda>
 #' * <https://github.com/msberends/AMR/raw/master/data/antivirals.rda>
@@ -71,19 +71,19 @@
 #'
 #' WHONET 2019 software: <http://www.whonet.org/software.html>
 #'
-#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <http://ec.europa.eu/health/documents/community-register/html/atc.htm>
-#' @inheritSection AMR Reference data publicly available
+#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: <https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm>
+#' @inheritSection AMR Reference Data Publicly Available
 #' @inheritSection WHOCC WHOCC
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @seealso [microorganisms], [intrinsic_resistant]
 "antibiotics"

 #' @rdname antibiotics
 "antivirals"

-#' Data set with `r format(nrow(microorganisms), big.mark = ",")` microorganisms
+#' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
 #'
-#' 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, last updated in `r CATALOGUE_OF_LIFE$yearmonth_LPSN`, of six kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
 #' @inheritSection catalogue_of_life Catalogue of Life
 #' @format A [data.frame] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
 #' - `mo`\cr ID of microorganism as used by this package
@@ -92,10 +92,17 @@
 #' - `rank`\cr Text of the taxonomic rank of the microorganism, like `"species"` or `"genus"`
 #' - `ref`\cr Author(s) and year of concerning scientific publication
 #' - `species_id`\cr ID of the species as used by the Catalogue of Life
-#' - `source`\cr Either "CoL", "DSMZ" (see Source) or "manually added"
+#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
 #' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
-#' - `snomed`\cr SNOMED code of the microorganism. Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
-#' @details Manually added were:
+#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, according to the `r SNOMED_VERSION$current_source` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
+#' @details 
+#' Please note that entries are only based on the Catalogue of Life and the LPSN (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
+#' 
+#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not before 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
+#' 
+#' ## Manual additions
+#' For convenience, some entries were added manually:
+#' 
 #' - 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)
@@ -103,40 +110,40 @@
 #' - 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 (yet) in the Catalogue of Life
-#' - `r format(nrow(subset(microorganisms, source == "DSMZ")), big.mark = ",")` species from the DSMZ (Deutsche Sammlung von Mikroorganismen und Zellkulturen) since the DSMZ contain the latest taxonomic information based on recent publications
 #' 
-#' ### Direct download
-#' This data set is available as 'flat file' for use even without R - you can find the file here:
+#' ## Direct download
+#' This data set is available as 'flat file' for use even without \R - you can find the file here:
 #' 
 #' * <https://github.com/msberends/AMR/raw/master/data-raw/microorganisms.txt>
 #' 
-#' The file in R format (with preserved data structure) can be found here:
+#' The file in \R format (with preserved data structure) can be found here:
 #' 
 #' * <https://github.com/msberends/AMR/raw/master/data/microorganisms.rda>
-#' @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 <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date>)*
+#' @section About the Records from LPSN (see *Source*):
+#' The List of Prokaryotic names with Standing in Nomenclature (LPSN) provides comprehensive information on the nomenclature of prokaryotes. LPSN is a free to use service founded by Jean P. Euzeby in 1997 and later on maintained by Aidan C. Parte.
 #' 
-#' In February 2020, the DSMZ records were merged with the List of Prokaryotic names with Standing in Nomenclature (LPSN).
-#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
+#' As of February 2020, the regularly augmented LPSN database at DSMZ is the basis of the new LPSN service. The new database was implemented for the Type-Strain Genome Server and augmented in 2018 to store all kinds of nomenclatural information. Data from the previous version of LPSN and from the Prokaryotic Nomenclature Up-to-date (PNU) service were imported into the new system. PNU had been established in 1993 as a service of the Leibniz Institute DSMZ, and was curated by Norbert Weiss, Manfred Kracht and Dorothea Gleim.
+#' @source 
+#' `r gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE)` as currently implemented in this `AMR` package:
 #' 
-#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: 10.1099/ijsem.0.002786
-#'
-#' Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Germany, Prokaryotic Nomenclature Up-to-Date, <https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date> and <https://lpsn.dsmz.de> (check included version with [catalogue_of_life_version()]).
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' * Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org>
+#' 
+#' List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`) as currently implemented in this `AMR` package:
+#' 
+#' * Parte, A.C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Goker, M. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}
+#' * Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
+#' * Parte, A.C. (2014). LPSN — List of Prokaryotic names with Standing in Nomenclature. Nucleic Acids Research, 42, Issue D1, D613–D616; \doi{10.1093/nar/gkt1111}
+#' * Euzeby, J.P. (1997). List of Bacterial Names with Standing in Nomenclature: a Folder Available on the Internet. International Journal of Systematic Bacteriology, 47, 590-592; \doi{10.1099/00207713-47-2-590}
+#' 
+#' `r SNOMED_VERSION$current_source` as currently implemented in this `AMR` package:
+#' 
+#' * Retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
 "microorganisms"

-catalogue_of_life <- list(
-  year = 2019,
-  version = "Catalogue of Life: {year} Annual Checklist",
-  url_CoL = "http://www.catalogueoflife.org/col/",
-  url_DSMZ = "https://lpsn.dsmz.de",
-  yearmonth_DSMZ = "May 2020"
-)
-
-#' Data set with previously accepted taxonomic names
+#' Data Set with Previously Accepted Taxonomic Names
 #'
 #' A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by [as.mo()].
 #' @inheritSection catalogue_of_life Catalogue of Life
@@ -147,56 +154,56 @@ catalogue_of_life <- list(
 #' - `prevalence`\cr 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()]).
 #' 
-#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; doi: 10.1099/ijsem.0.002786
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' Parte, A.C. (2018). LPSN — List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @seealso [as.mo()] [mo_property()] [microorganisms]
 "microorganisms.old"

-#' Data set with `r format(nrow(microorganisms.codes), big.mark = ",")` common microorganism codes
+#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
 #'
 #' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with [set_mo_source()]. They will all be searched when using [as.mo()] and consequently all the [`mo_*`][mo_property()] functions.
 #' @format A [data.frame] with `r format(nrow(microorganisms.codes), big.mark = ",")` observations and `r ncol(microorganisms.codes)` variables:
 #' - `code`\cr Commonly used code of a microorganism
 #' - `mo`\cr ID of the microorganism in the [microorganisms] data set
-#' @inheritSection AMR Reference data publicly available
+#' @inheritSection AMR Reference Data Publicly Available
 #' @inheritSection catalogue_of_life Catalogue of Life
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @seealso [as.mo()] [microorganisms]
 "microorganisms.codes"

-#' Data set with `r format(nrow(example_isolates), big.mark = ",")` example isolates
+#' Data Set with `r format(nrow(example_isolates), big.mark = ",")` Example Isolates
 #'
-#' A data set containing `r format(nrow(example_isolates), big.mark = ",")` 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.github.io/AMR/articles/AMR.html).
+#' A data set containing `r format(nrow(example_isolates), big.mark = ",")` microbial isolates with their full antibiograms. The data set reflects reality and can be used to practice AMR data analysis. For examples, please read [the tutorial on our website](https://msberends.github.io/AMR/articles/AMR.html).
 #' @format A [data.frame] with `r format(nrow(example_isolates), big.mark = ",")` observations and `r ncol(example_isolates)` 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
+#' - `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 `r sum(sapply(example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' - `PEN:RIF`\cr `r sum(vapply(FUN.VALUE = logical(1), example_isolates, is.rsi))` different antibiotics with class [`rsi`] (see [as.rsi()]); these column names occur in the [antibiotics] data set and can be translated with [ab_name()]
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 "example_isolates"

-#' Data set with unclean data
+#' Data Set with Unclean Data
 #'
-#' A data set containing `r format(nrow(example_isolates_unclean), big.mark = ",")` microbial isolates that are not cleaned up and consequently not ready for AMR analysis. This data set can be used for practice.
+#' A data set containing `r format(nrow(example_isolates_unclean), big.mark = ",")` microbial isolates that are not cleaned up and consequently not ready for AMR data analysis. This data set can be used for practice.
 #' @format A [data.frame] with `r format(nrow(example_isolates_unclean), big.mark = ",")` observations and `r ncol(example_isolates_unclean)` variables:
 #' - `patient_id`\cr ID of the patient
 #' - `date`\cr date of receipt at the laboratory
 #' - `hospital`\cr ID of the hospital, from A to C
 #' - `bacteria`\cr info about microorganism that can be transformed with [as.mo()], see also [microorganisms]
 #' - `AMX:GEN`\cr 4 different antibiotics that have to be transformed with [as.rsi()]
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 "example_isolates_unclean"

-#' Data set with `r format(nrow(WHONET), big.mark = ",")` isolates - WHONET example
+#' Data Set with `r format(nrow(WHONET), big.mark = ",")` 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 antibiotic results are from our [example_isolates] data set. All patient names are created using online surname generators and are only in place for practice purposes.
 #' @format A [data.frame] with `r format(nrow(WHONET), big.mark = ",")` observations and `r ncol(WHONET)` variables:
@@ -210,8 +217,8 @@ catalogue_of_life <- list(
 #' - `Sex`\cr Fictitious gender of patient
 #' - `Age`\cr Fictitious 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
+#' - `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
@@ -224,18 +231,18 @@ catalogue_of_life <- list(
 #' - `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 `r sum(sapply(WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations 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 Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' - `Date of data entry`\cr [Date] this data was entered in WHONET
+#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.rsi))` different antibiotics. You can lookup the abbreviations 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 Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 "WHONET"

-#' Data set for R/SI interpretation
+#' Data Set for R/SI Interpretation
 #'
 #' Data set to interpret MIC and disk diffusion to R/SI values. Included guidelines are CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`) and EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
 #' @format A [data.frame] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables:
 #' - `guideline`\cr Name of the guideline
-#' - `method`\cr Either "MIC" or "DISK"
+#' - `method`\cr Either `r vector_or(rsi_translation$method)`
 #' - `site`\cr Body site, e.g. "Oral" or "Respiratory"
 #' - `mo`\cr Microbial ID, see [as.mo()]
 #' - `ab`\cr Antibiotic ID, see [as.ab()]
@@ -243,14 +250,14 @@ catalogue_of_life <- list(
 #' - `disk_dose`\cr Dose of the used disk diffusion method
 #' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
 #' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
-#' - `uti`\cr A logical value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
+#' - `uti`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
 #' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically.
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @seealso [intrinsic_resistant]
 "rsi_translation"

-#' Data set with bacterial intrinsic resistance
+#' Data Set with Bacterial Intrinsic Resistance
 #'
 #' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
 #' @format A [data.frame] with `r format(nrow(intrinsic_resistant), big.mark = ",")` observations and `r ncol(intrinsic_resistant)` variables:
@@ -258,14 +265,34 @@ catalogue_of_life <- list(
 #' - `antibiotic`\cr Name of the antibiotic drug
 #' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/intrinsic_resistant.txt>. This file **allows for machine reading EUCAST guidelines about intrinsic resistance**, which is almost impossible with the Excel and PDF files distributed by EUCAST. The file is updated automatically.
 #' 
-#' This data set is based on '`r EUCAST_VERSION_EXPERT_RULES[["3.2"]]$title`', `r EUCAST_VERSION_EXPERT_RULES[["3.2"]]$version_txt` from `r EUCAST_VERSION_EXPERT_RULES[["3.2"]]$year`.
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' This data set is based on `r format_eucast_version_nr(3.2)`.
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
+#' \donttest{
 #' if (require("dplyr")) {
 #'   intrinsic_resistant %>%
 #'     filter(antibiotic == "Vancomycin", microorganism %like% "Enterococcus") %>% 
 #'     pull(microorganism)
 #'   # [1] "Enterococcus casseliflavus" "Enterococcus gallinarum"
 #' }
+#' }
 "intrinsic_resistant"
+
+#' Data Set with Treatment Dosages as Defined by EUCAST
+#'
+#' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()].
+#' @format A [data.frame] with `r format(nrow(dosage), big.mark = ",")` observations and `r ncol(dosage)` variables:
+#' - `ab`\cr Antibiotic ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
+#' - `name`\cr Official name of the antimicrobial agent as used by WHONET/EARS-Net or the WHO
+#' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)`
+#' - `dose`\cr Dose, such as "2 g" or "25 mg/kg"
+#' - `dose_times`\cr Number of times a dose must be administered
+#' - `administration`\cr Route of administration, either `r vector_or(dosage$administration)`
+#' - `notes`\cr Additional dosage notes
+#' - `original_txt`\cr Original text in the PDF file of EUCAST
+#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply
+#' @details `r format_eucast_version_nr(11.0)` are based on the dosages in this data set.
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
+"dosage"
--- a/R/deprecated.R
+++ b/R/deprecated.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,14 +20,483 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Deprecated functions
+#' Deprecated Functions
 #'
-#' 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 lifecycle Retired lifecycle
-#' @inheritSection AMR Read more on our website!
+#' 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).
+#' @details All antibiotic class selectors (such as [carbapenems()], [aminoglycosides()]) can now be used for filtering as well, making all their accompanying `filter_*()` functions redundant (such as [filter_carbapenems()], [filter_aminoglycosides()]).
+#' @inheritSection lifecycle Retired Lifecycle
+#' @inheritSection AMR Read more on Our Website!
 #' @keywords internal
 #' @name AMR-deprecated
-# @export
+#' @export
+p_symbol <- function(p, emptychar = " ") {
+  .Deprecated(package = "AMR", new = "cleaner::p_symbol")
+  
+  p <- as.double(p)
+  s <- rep(NA_character_, length(p))
+  
+  s[p <= 1] <- emptychar
+  s[p <= 0.100] <- "."
+  s[p <= 0.050] <- "*"
+  s[p <= 0.010] <- "**"
+  s[p <= 0.001] <- "***"
+  
+  s
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_first_weighted_isolate <- function(x = NULL,
+                                          col_date = NULL,
+                                          col_patient_id = NULL,
+                                          col_mo = NULL,
+                                          ...) {
+  
+  .Deprecated(old = "filter_first_weighted_isolate()",
+              new = "filter_first_isolate()",
+              package = "AMR")
+  
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
+  meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  
+  filter_first_isolate(x = x, col_date = col_date, col_patient_id = col_patient_id, col_mo = col_mo, ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+key_antibiotics <- function(x = NULL,
+                            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"),
+                            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,
+                            ...) {
+  
+  .Deprecated(old = "key_antibiotics()",
+              new = "key_antimicrobials()",
+              package = "AMR")
+  
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+
+  key_antimicrobials(x = x, 
+                     col_mo = col_mo, 
+                     universal = c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6),
+                     gram_negative = c(GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6),
+                     gram_positive = c(GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6),
+                     antifungal = NULL,
+                     only_rsi_columns = FALSE,
+                     ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+key_antibiotics_equal <- function(y,
+                                  z,
+                                  type = "keyantimicrobials",
+                                  ignore_I = TRUE,
+                                  points_threshold = 2,
+                                  info = FALSE,
+                                  na.rm = TRUE,
+                                  ...) {
+  
+  .Deprecated(old = "key_antibiotics_equal()",
+              new = "antimicrobials_equal()",
+              package = "AMR")
+  
+  antimicrobials_equal(y = y,
+                       z = z,
+                       type = type,
+                       ignore_I = ignore_I,
+                       points_threshold = points_threshold,
+                       info = info)
+}
+
+
+#' @name AMR-deprecated
+#' @export
+filter_ab_class <- function(x,
+                            ab_class,
+                            result = NULL,
+                            scope = "any",
+                            only_rsi_columns = FALSE,
+                            ...) {
+  
+  .call_depth <- list(...)$`.call_depth`
+  if (is.null(.call_depth)) {
+    .call_depth <- 0
+  }
+  .x_name <- list(...)$`.x_name`
+  if (is.null(.x_name)) {
+    .x_name <- deparse(substitute(x))
+  }
+  .fn <- list(...)$`.fn`
+  if (is.null(.fn)) {
+    .fn <- "filter_ab_class"
+  }
+  .fn_old <- .fn
+  # new way: using the ab selectors
+  .fn <- gsub("filter_", "", .fn, fixed = TRUE)
+  .fn <- gsub("^([1-5][a-z]+)_cephalosporins", "cephalosporins_\\1", .fn)
+  
+  if (missing(x) || is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- get_current_data(arg_name = "x", call = -2 - .call_depth)
+    .x_name <- "your_data"
+  }
+  meet_criteria(x, allow_class = "data.frame", .call_depth = .call_depth)
+  meet_criteria(ab_class, allow_class = "character", has_length = 1, .call_depth = .call_depth)
+  if (!is.null(result)) {
+    # make result = "SI" works too:
+    result <- toupper(unlist(strsplit(result, "")))
+  }
+  meet_criteria(result, allow_class = "character", has_length = c(1, 2, 3), is_in = c("S", "I", "R"), allow_NULL = TRUE, .call_depth = .call_depth)
+  meet_criteria(scope, allow_class = "character", has_length = 1, is_in = c("all", "any"), .call_depth = .call_depth)
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1, .call_depth = .call_depth)
+  
+  if (is.null(result)) {
+    result <- c("S", "I", "R")
+  }
+  
+  # get e.g. carbapenems() from filter_carbapenems()
+  fn <- get(.fn, envir = asNamespace("AMR"))
+  if (scope == "any") {
+    scope_fn <- any
+  } else {
+    scope_fn <- all
+  }
+  
+  # be nice here, be VERY extensive about how the AB selectors have taken over this function
+  deprecated_fn <- paste0(.fn, "(", ifelse(.fn == "ab_class", paste0("\"", ab_class, "\""), ""), ")",
+                          ifelse(length(result) > 1,
+                                 paste0(", c(", paste0("\"", result, "\"", collapse = ", "), ")"),
+                                 ifelse(is.null(result),
+                                        "",
+                                        paste0(" == \"", result, "\""))))
+  if (.x_name == ".") {
+    .x_name <- "your_data"
+  }
+  warning_(paste0("`", .fn_old, "()` is deprecated. Use the antibiotic selector `", .fn, "()` instead.\n",
+                  "In dplyr:\n",
+                  "  - ", .x_name, " %>% filter(", scope, "(", deprecated_fn, "))\n",
+                  ifelse(length(result) > 1, 
+                         paste0("  - ", .x_name, " %>% filter(", scope, "(", 
+                                .fn, "(", ifelse(.fn == "ab_class", paste0("\"", ab_class, "\""), ""), ") == \"R\"))\n"),
+                         ""),
+                  "In base R:\n",
+                  "  - ", .x_name, "[", scope, "(", deprecated_fn, "), ]\n",
+                  ifelse(length(result) > 1, 
+                         paste0("  - ", .x_name, "[", scope, "(", 
+                                .fn, "(", ifelse(.fn == "ab_class", paste0("\"", ab_class, "\""), ""), ") == \"R\"), ]\n"),
+                         ""),
+                  "  - subset(", .x_name, ", ", scope, "(", deprecated_fn, "))",
+                  ifelse(length(result) > 1, 
+                         paste0("\n  - subset(", .x_name, ", ", scope, "(", 
+                                .fn, "(", ifelse(.fn == "ab_class", paste0("\"", ab_class, "\""), ""), ") == \"R\"))"),
+                         "")),
+           call = FALSE)
+  
+  if (.fn == "ab_class") {
+    subset(x, scope_fn(fn(ab_class = ab_class), result))
+  } else {
+    subset(x, scope_fn(fn(), result))
+  }
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_aminoglycosides <- function(x,
+                                   result = NULL,
+                                   scope = "any",
+                                   only_rsi_columns = FALSE,
+                                   ...) {
+  filter_ab_class(x = x,
+                  ab_class = "aminoglycoside",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_aminoglycosides",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_betalactams <- function(x,
+                               result = NULL,
+                               scope = "any",
+                               only_rsi_columns = FALSE,
+                               ...) {
+  filter_ab_class(x = x,
+                  ab_class = "carbapenem|cephalosporin|penicillin",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_betalactams",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+#' @name AMR-deprecated
+#' @export
+filter_carbapenems <- function(x,
+                               result = NULL,
+                               scope = "any",
+                               only_rsi_columns = FALSE,
+                               ...) {
+  filter_ab_class(x = x,
+                  ab_class = "carbapenem",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_carbapenems",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_cephalosporins <- function(x,
+                                  result = NULL,
+                                  scope = "any",
+                                  only_rsi_columns = FALSE,
+                                  ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporin",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_1st_cephalosporins <- function(x,
+                                      result = NULL,
+                                      scope = "any",
+                                      only_rsi_columns = FALSE,
+                                      ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporins (1st gen.)",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_1st_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_2nd_cephalosporins <- function(x,
+                                      result = NULL,
+                                      scope = "any",
+                                      only_rsi_columns = FALSE,
+                                      ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporins (2nd gen.)",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_2nd_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_3rd_cephalosporins <- function(x,
+                                      result = NULL,
+                                      scope = "any",
+                                      only_rsi_columns = FALSE,
+                                      ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporins (3rd gen.)",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_3rd_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_4th_cephalosporins <- function(x,
+                                      result = NULL,
+                                      scope = "any",
+                                      only_rsi_columns = FALSE,
+                                      ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporins (4th gen.)",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_4th_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_5th_cephalosporins <- function(x,
+                                      result = NULL,
+                                      scope = "any",
+                                      only_rsi_columns = FALSE,
+                                      ...) {
+  filter_ab_class(x = x,
+                  ab_class = "cephalosporins (5th gen.)",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_5th_cephalosporins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_fluoroquinolones <- function(x,
+                                    result = NULL,
+                                    scope = "any",
+                                    only_rsi_columns = FALSE,
+                                    ...) {
+  filter_ab_class(x = x,
+                  ab_class = "fluoroquinolone",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_fluoroquinolones",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_glycopeptides <- function(x,
+                                 result = NULL,
+                                 scope = "any",
+                                 only_rsi_columns = FALSE,
+                                 ...) {
+  filter_ab_class(x = x,
+                  ab_class = "glycopeptide",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_glycopeptides",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_macrolides <- function(x,
+                              result = NULL,
+                              scope = "any",
+                              only_rsi_columns = FALSE,
+                              ...) {
+  filter_ab_class(x = x,
+                  ab_class = "macrolide",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_macrolides",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_oxazolidinones <- function(x,
+                                  result = NULL,
+                                  scope = "any",
+                                  only_rsi_columns = FALSE,
+                                  ...) {
+  filter_ab_class(x = x,
+                  ab_class = "oxazolidinone",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_oxazolidinones",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_penicillins <- function(x,
+                               result = NULL,
+                               scope = "any",
+                               only_rsi_columns = FALSE,
+                               ...) {
+  filter_ab_class(x = x,
+                  ab_class = "penicillin",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_penicillins",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
+
+#' @name AMR-deprecated
+#' @export
+filter_tetracyclines <- function(x,
+                                 result = NULL,
+                                 scope = "any",
+                                 only_rsi_columns = FALSE,
+                                 ...) {
+  filter_ab_class(x = x,
+                  ab_class = "tetracycline",
+                  result = result,
+                  scope = scope,
+                  only_rsi_columns = only_rsi_columns,
+                  .call_depth = 1,
+                  .fn = "filter_tetracyclines",
+                  .x_name = deparse(substitute(x)),
+                  ...)
+}
--- a/R/disk.R
+++ b/R/disk.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,26 +20,25 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Transform input to disk diffusion diameters
+#' Transform Input to Disk Diffusion Diameters
 #'
 #' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50.
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @rdname as.disk
 #' @param x vector
-#' @param na.rm a logical indicating whether missing values should be removed
+#' @param na.rm a [logical] indicating whether missing values should be removed
 #' @details Interpret disk values as RSI values with [as.rsi()]. It supports guidelines from EUCAST and CLSI.
 #' @return An [integer] with additional class [`disk`]
 #' @aliases disk
 #' @export
 #' @seealso [as.rsi()]
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' \donttest{
 #' # transform existing disk zones to the `disk` class
-#' library(dplyr)
 #' df <- data.frame(microorganism = "E. coli",
 #'                  AMP = 20,
 #'                  CIP = 14,
@@ -58,8 +57,11 @@
 #' as.rsi(df)
 #' }
 as.disk <- function(x, na.rm = FALSE) {
+  meet_criteria(x, allow_class = c("disk", "character", "numeric", "integer"), allow_NA = TRUE)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  
  if (!is.disk(x)) {
-    x <- x %pm>% unlist()
+    x <- unlist(x)
    if (na.rm == TRUE) {
      x <- x[!is.na(x)]
    }
@@ -67,13 +69,13 @@ as.disk <- function(x, na.rm = FALSE) {
    
    na_before <- length(x[is.na(x)])
    
-    # heavily based on the function from our cleaner package:
+    # heavily based on cleaner::clean_double():
    clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) {
      x <- gsub(",", ".", x)
      # remove ending dot/comma
      x <- gsub("[,.]$", "", x)
      # only keep last dot/comma
-      reverse <- function(x) sapply(lapply(strsplit(x, NULL), rev), paste, collapse = "")
+      reverse <- function(x) vapply(FUN.VALUE = character(1), lapply(strsplit(x, NULL), rev), paste, collapse = "")
      x <- sub("{{dot}}", ".", 
               gsub(".", "",
                    reverse(sub(".", "}}tod{{",
@@ -83,7 +85,7 @@ as.disk <- function(x, na.rm = FALSE) {
               fixed = TRUE)
      x_clean <- gsub(remove, "", x, ignore.case = TRUE, fixed = fixed)
      # remove everything that is not a number or dot
-      as.numeric(gsub("[^0-9.]+", "", x_clean))
+      as.double(gsub("[^0-9.]+", "", x_clean))
    }
    
    # round up and make it an integer
@@ -96,21 +98,25 @@ as.disk <- function(x, na.rm = FALSE) {
    if (na_before != na_after) {
      list_missing <- x.bak[is.na(x) & !is.na(x.bak)] %pm>%
        unique() %pm>%
-        sort()
-      list_missing <- paste0('"', list_missing, '"', collapse = ", ")
-      warning(na_after - na_before, " results truncated (",
-              round(((na_after - na_before) / length(x)) * 100),
-              "%) that were invalid disk zones: ",
-              list_missing, call. = FALSE)
+        sort() %pm>%
+        vector_and(quotes = TRUE)
+      warning_(na_after - na_before, " results truncated (",
+               round(((na_after - na_before) / length(x)) * 100),
+               "%) that were invalid disk zones: ",
+               list_missing, call = FALSE)
    }
  }
-  structure(as.integer(x),
-            class = c("disk", "integer"))
+  set_clean_class(as.integer(x),
+                  new_class = c("disk", "integer"))
 }

 all_valid_disks <- function(x) {
-  x_disk <- suppressWarnings(as.disk(x[!is.na(x)]))
-  !any(is.na(x_disk)) & !all(is.na(x))
+  if (!inherits(x, c("disk", "character", "numeric", "integer"))) {
+    return(FALSE)
+  }
+  x_disk <- tryCatch(suppressWarnings(as.disk(x[!is.na(x)])),
+                     error = function(e) NA)
+  !any(is.na(x_disk)) && !all(is.na(x))
 }

 #' @rdname as.disk
@@ -176,11 +182,8 @@ print.disk <- function(x, ...) {
 #' @method c disk
 #' @export
 #' @noRd
-c.disk <- function(x, ...) {
-  y <- NextMethod()
-  y <- as.disk(y)
-  attributes(y) <- attributes(x)
-  y
+c.disk <- function(...) {
+  as.disk(unlist(lapply(list(...), as.character)))
 }

 #' @method unique disk
@@ -194,14 +197,12 @@ unique.disk <- function(x, incomparables = FALSE, ...) {

 # will be exported using s3_register() in R/zzz.R
 get_skimmers.disk <- function(column) {
-  sfl <- import_fn("sfl", "skimr", error_on_fail = FALSE)
-  inline_hist <- import_fn("inline_hist", "skimr", error_on_fail = FALSE)
-  sfl(
+  skimr::sfl(
    skim_type = "disk",
    min = ~min(as.double(.), na.rm = TRUE),
    max = ~max(as.double(.), na.rm = TRUE),
    median = ~stats::median(as.double(.), na.rm = TRUE),
-    n_unique = ~pm_n_distinct(., na.rm = TRUE),
-    hist = ~inline_hist(stats::na.omit(as.double(.)))
+    n_unique = ~length(unique(stats::na.omit(.))),
+    hist = ~skimr::inline_hist(stats::na.omit(as.double(.)))
  )
 }
--- a/R/episode.R
+++ b/R/episode.R
@@ -0,0 +1,191 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' Determine (New) Episodes for Patients
+#' 
+#' These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument `episode_days`. This can be used to determine clinical episodes for any epidemiological analysis. The [get_episode()] function returns the index number of the episode per group, while the [is_new_episode()] function returns values `TRUE`/`FALSE` to indicate whether an item in a vector is the start of a new episode.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x vector of dates (class `Date` or `POSIXt`)
+#' @param episode_days required episode length in days, can also be less than a day or `Inf`, see *Details*
+#' @param ... ignored, only in place to allow future extensions
+#' @details 
+#' Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least `episode_days` days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least `episode_days` days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.
+#' 
+#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.
+#' 
+#' The `dplyr` package is not required for these functions to work, but these functions do support [variable grouping][dplyr::group_by()] and work conveniently inside `dplyr` verbs such as [`filter()`][dplyr::filter()], [`mutate()`][dplyr::mutate()] and [`summarise()`][dplyr::summarise()].
+#' @return 
+#' * [get_episode()]: a [double] vector
+#' * [is_new_episode()]: a [logical] vector
+#' @seealso [first_isolate()]
+#' @rdname get_episode
+#' @export
+#' @inheritSection AMR Read more on Our Website!
+#' @examples
+#' # `example_isolates` is a data set available in the AMR package.
+#' # See ?example_isolates.
+#' 
+#' get_episode(example_isolates$date, episode_days = 60)    # indices
+#' is_new_episode(example_isolates$date, episode_days = 60) # TRUE/FALSE
+#' 
+#' # filter on results from the third 60-day episode only, using base R
+#' example_isolates[which(get_episode(example_isolates$date, 60) == 3), ]
+#' 
+#' # the functions also work for less than a day, e.g. to include one per hour:
+#' get_episode(c(Sys.time(),
+#'               Sys.time() + 60 * 60),
+#'             episode_days = 1/24)
+#' 
+#' \donttest{
+#' if (require("dplyr")) {
+#'   # is_new_episode() can also be used in dplyr verbs to determine patient
+#'   # episodes based on any (combination of) grouping variables:
+#'   example_isolates %>%
+#'     mutate(condition = sample(x = c("A", "B", "C"), 
+#'                               size = 2000,
+#'                               replace = TRUE)) %>% 
+#'     group_by(condition) %>%
+#'     mutate(new_episode = is_new_episode(date, 365))
+#'     
+#'   example_isolates %>%
+#'     group_by(hospital_id, patient_id) %>%
+#'     transmute(date, 
+#'               patient_id,
+#'               new_index = get_episode(date, 60),
+#'               new_logical = is_new_episode(date, 60))
+#'   
+#'   
+#'   example_isolates %>%
+#'     group_by(hospital_id) %>% 
+#'     summarise(patients = n_distinct(patient_id),
+#'               n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
+#'               n_episodes_60  = sum(is_new_episode(date, episode_days = 60)),
+#'               n_episodes_30  = sum(is_new_episode(date, episode_days = 30)))
+#'     
+#'     
+#'   # grouping on patients and microorganisms leads to the same
+#'   # results as first_isolate() when using 'episode-based':
+#'   x <- example_isolates %>%
+#'     filter_first_isolate(include_unknown = TRUE,
+#'                          method = "episode-based")
+#'     
+#'   y <- example_isolates %>%
+#'     group_by(patient_id, mo) %>%
+#'     filter(is_new_episode(date, 365))
+#'
+#'   identical(x$patient_id, y$patient_id)
+#'   
+#'   # but is_new_episode() has a lot more flexibility than first_isolate(),
+#'   # since you can now group on anything that seems relevant:
+#'   example_isolates %>%
+#'     group_by(patient_id, mo, hospital_id, ward_icu) %>%
+#'     mutate(flag_episode = is_new_episode(date, 365))
+#' }
+#' }
+get_episode <- function(x, episode_days, ...) {
+  meet_criteria(x, allow_class = c("Date", "POSIXt"))
+  meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE)
+  
+  exec_episode(type = "sequential",
+               x = x, 
+               episode_days = episode_days,
+               ... = ...)
+}
+
+#' @rdname get_episode
+#' @export
+is_new_episode <- function(x, episode_days, ...) {
+  meet_criteria(x, allow_class = c("Date", "POSIXt"))
+  meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE)
+  
+  exec_episode(type = "logical",
+               x = x, 
+               episode_days = episode_days,
+               ... = ...)
+}
+
+exec_episode <- function(type, x, episode_days, ...) {
+  x <- as.double(as.POSIXct(x)) # as.POSIXct() required for Date classes
+  # since x is now in seconds, get seconds from episode_days as well
+  episode_seconds <- episode_days * 60 * 60 * 24
+  
+  if (length(x) == 1) {
+    if (type == "logical") {
+      return(TRUE)
+    } else if (type == "sequential") {
+      return(1)
+    }
+  } else if (length(x) == 2) {
+    if (max(x) - min(x) >= episode_seconds) {
+      if (type == "logical") {
+        return(c(TRUE, TRUE))
+      } else if (type == "sequential") {
+        return(c(1, 2))
+      }
+    } else {
+      if (type == "logical") {
+        return(c(TRUE, FALSE))
+      } else if (type == "sequential") {
+        return(c(1, 1))
+      }
+    }
+  }
+  
+  # I asked on StackOverflow:
+  # https://stackoverflow.com/questions/42122245/filter-one-row-every-year
+  exec <- function(x, episode_seconds) {
+    indices <- integer()
+    start <- x[1]
+    ind <- 1
+    indices[1] <- 1
+    for (i in 2:length(x)) {
+      if (isTRUE((x[i] - start) >= episode_seconds)) {
+        ind <- ind + 1
+        if (type == "logical") {
+          indices[ind] <- i
+        }
+        start <- x[i]
+      }
+      if (type == "sequential") {
+        indices[i] <- ind
+      }
+    }
+    if (type == "logical") {
+      result <- rep(FALSE, length(x))
+      result[indices] <- TRUE
+      result
+    } else if (type == "sequential") {
+      indices
+    }
+  }
+  
+  df <- data.frame(x = x,
+                   y = seq_len(length(x))) %pm>%
+    pm_arrange(x)
+  df$new <- exec(df$x, episode_seconds)
+  df %pm>%
+    pm_arrange(y) %pm>%
+    pm_pull(new)
+}
--- a/R/eucast_rules.R
+++ b/R/eucast_rules.R
--- a/R/filter_ab_class.R
+++ b/R/filter_ab_class.R
@@ -1,364 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
-# Developed at the University of Groningen, the Netherlands, in        #
-# collaboration with non-profit organisations Certe Medical            #
-# Diagnostics & Advice, and University Medical Center Groningen.       # 
-#                                                                      #
-# This R package is free software; you can freely use and distribute   #
-# it for both personal and commercial purposes under the terms of the  #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
-# the Free Software Foundation.                                        #
-# We created this package for both routine data analysis and academic  #
-# research and it was publicly released in the hope that it will be    #
-# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
-#                                                                      #
-# Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
-# ==================================================================== #
-
-#' Filter isolates on result in antimicrobial class
-#'
-#' Filter isolates on results in specific antimicrobial classes. This makes it easy to filter on isolates that were tested for e.g. any aminoglycoside, or to filter on carbapenem-resistant isolates without the need to specify the drugs.
-#' @inheritSection lifecycle Stable lifecycle
-#' @param x a data set
-#' @param ab_class an antimicrobial class, like `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
-#' @param result an antibiotic result: S, I or R (or a combination of more of them)
-#' @param scope the scope to check which variables to check, can be `"any"` (default) or `"all"`
-#' @param ... previously used when this package still depended on the `dplyr` package, now ignored
-#' @details All columns of `x` will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a filter function like e.g. [filter_aminoglycosides()] will include column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
-#' @rdname filter_ab_class
-#' @seealso [antibiotic_class_selectors()] for the `select()` equivalent.
-#' @export
-#' @examples
-#' filter_aminoglycosides(example_isolates)
-#' 
-#' \donttest{
-#' if (require("dplyr")) {
-#'
-#'   # filter on isolates that have any result for any aminoglycoside
-#'   example_isolates %>% filter_aminoglycosides()
-#'   example_isolates %>% filter_ab_class("aminoglycoside")
-#' 
-#'   # this is essentially the same as (but without determination of column names):
-#'   example_isolates %>%
-#'     filter_at(.vars = vars(c("GEN", "TOB", "AMK", "KAN")),
-#'               .vars_predicate = any_vars(. %in% c("S", "I", "R")))
-#'
-#'
-#'   # filter on isolates that show resistance to ANY aminoglycoside
-#'   example_isolates %>% filter_aminoglycosides("R", "any")
-#'  
-#'   # filter on isolates that show resistance to ALL aminoglycosides
-#'   example_isolates %>% filter_aminoglycosides("R", "all")
-#'  
-#'   # filter on isolates that show resistance to
-#'   # any aminoglycoside and any fluoroquinolone
-#'   example_isolates %>%
-#'     filter_aminoglycosides("R") %>%
-#'     filter_fluoroquinolones("R")
-#'  
-#'   # filter on isolates that show resistance to
-#'   # all aminoglycosides and all fluoroquinolones
-#'   example_isolates %>%
-#'     filter_aminoglycosides("R", "all") %>%
-#'     filter_fluoroquinolones("R", "all")
-#'   
-#'   # with dplyr 1.0.0 and higher (that adds 'across()'), this is equal:
-#'   example_isolates %>% filter_carbapenems("R", "all")
-#'   example_isolates %>% filter(across(carbapenems(), ~. == "R"))
-#' }
-#' }
-filter_ab_class <- function(x,
-                            ab_class,
-                            result = NULL,
-                            scope = "any",
-                            ...) {
-  
-  check_dataset_integrity()
-  stop_ifnot(is.data.frame(x), "`x` must be a data frame")
-  
-  # save to return later
-  x_class <- class(x)
-  x.bak <- x
-  x <- as.data.frame(x, stringsAsFactors = FALSE)
-  
-  scope <- scope[1L]
-  if (is.null(result)) {
-    result <- c("S", "I", "R")
-  }
-  # make result = "SI" works too:
-  result <- unlist(strsplit(result, ""))
-  
-  stop_ifnot(all(result %in% c("S", "I", "R")), "`result` must be one or more of: 'S', 'I', 'R'")
-  stop_ifnot(all(scope %in% c("any", "all")), "`scope` must be one of: 'any', 'all'")
-  
-  # get all columns in data with names that resemble antibiotics
-  ab_in_data <- get_column_abx(x, info = FALSE)
-  if (length(ab_in_data) == 0) {
-    message(font_blue("NOTE: no columns with class <rsi> found (see ?as.rsi), data left unchanged."))
-    return(x.bak)
-  }
-  # get reference data
-  ab_class.bak <- ab_class
-  ab_class <- gsub("[^a-zA-Z0-9]+", ".*", ab_class)
-  ab_class <- gsub("(ph|f)", "(ph|f)", ab_class)
-  ab_class <- gsub("(t|th)", "(t|th)", ab_class)
-  ab_reference <- subset(antibiotics,
-                         group %like% ab_class | 
-                           atc_group1 %like% ab_class | 
-                           atc_group2 %like% ab_class)
-  ab_group <- find_ab_group(ab_class)
-  if (ab_group == "") {
-    message(font_blue(paste0("NOTE: unknown antimicrobial class '", ab_class.bak, "', data left unchanged.")))
-    return(x.bak)
-  }
-  # get the columns with a group names in the chosen ab class
-  agents <- ab_in_data[names(ab_in_data) %in% ab_reference$ab]
-  if (length(agents) == 0) {
-    message(font_blue(paste0("NOTE: no antimicrobial agents of class ", ab_group, 
-                             " found (such as ", find_ab_names(ab_class, 2), 
-                             "), data left unchanged.")))
-    return(x.bak)
-  }
-  
-  if (length(result) == 1) {
-    operator <- " is "
-  } else {
-    operator <- " is one of "
-  }
-  if (scope == "any") {
-    scope_txt <- " or "
-    scope_fn <- any
-  } else {
-    scope_txt <- " and "
-    scope_fn <- all
-    if (length(agents) > 1) {
-      operator <- gsub("is", "are", operator)
-    }
-  }
-  if (length(agents) > 1) {
-    scope <- paste(scope, "of columns ")
-  } else {
-    scope <- "column "
-  }
-  
-  # sort columns on official name
-  agents <- agents[order(ab_name(names(agents), language = NULL))]
-  
-  message(font_blue(paste0("Filtering on ", ab_group, ": ", scope,
-                           paste(paste0("`", font_bold(agents, collapse = NULL),
-                                        "` (", ab_name(names(agents), tolower = TRUE, language = NULL), ")"),
-                                 collapse = scope_txt),
-                           operator, toString(result))))
-  x_transposed <- as.list(as.data.frame(t(x[, agents, drop = FALSE])))
-  filtered <- sapply(x_transposed, function(y) scope_fn(y %in% result, na.rm = TRUE))
-  x <- x[which(filtered), , drop = FALSE]
-  class(x) <- x_class
-  x
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_aminoglycosides <- function(x,
-                                   result = NULL,
-                                   scope = "any",
-                                   ...) {
-  filter_ab_class(x = x,
-                  ab_class = "aminoglycoside",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_carbapenems <- function(x,
-                               result = NULL,
-                               scope = "any",
-                               ...) {
-  filter_ab_class(x = x,
-                  ab_class = "carbapenem",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_cephalosporins <- function(x,
-                                  result = NULL,
-                                  scope = "any",
-                                  ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporin",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_1st_cephalosporins <- function(x,
-                                      result = NULL,
-                                      scope = "any",
-                                      ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporins (1st gen.)",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_2nd_cephalosporins <- function(x,
-                                      result = NULL,
-                                      scope = "any",
-                                      ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporins (2nd gen.)",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_3rd_cephalosporins <- function(x,
-                                      result = NULL,
-                                      scope = "any",
-                                      ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporins (3rd gen.)",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_4th_cephalosporins <- function(x,
-                                      result = NULL,
-                                      scope = "any",
-                                      ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporins (4th gen.)",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_5th_cephalosporins <- function(x,
-                                      result = NULL,
-                                      scope = "any",
-                                      ...) {
-  filter_ab_class(x = x,
-                  ab_class = "cephalosporins (5th gen.)",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_fluoroquinolones <- function(x,
-                                    result = NULL,
-                                    scope = "any",
-                                    ...) {
-  filter_ab_class(x = x,
-                  ab_class = "fluoroquinolone",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_glycopeptides <- function(x,
-                                 result = NULL,
-                                 scope = "any",
-                                 ...) {
-  filter_ab_class(x = x,
-                  ab_class = "glycopeptide",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_macrolides <- function(x,
-                              result = NULL,
-                              scope = "any",
-                              ...) {
-  filter_ab_class(x = x,
-                  ab_class = "macrolide",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_penicillins <- function(x,
-                               result = NULL,
-                               scope = "any",
-                               ...) {
-  filter_ab_class(x = x,
-                  ab_class = "penicillin",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-#' @rdname filter_ab_class
-#' @export
-filter_tetracyclines <- function(x,
-                                 result = NULL,
-                                 scope = "any",
-                                 ...) {
-  filter_ab_class(x = x,
-                  ab_class = "tetracycline",
-                  result = result,
-                  scope = scope,
-                  ...)
-}
-
-find_ab_group <- function(ab_class) {
-  ab_class <- gsub("[^a-zA-Z0-9]", ".*", ab_class)
-  ifelse(ab_class %in% c("aminoglycoside",
-                         "carbapenem",
-                         "cephalosporin",
-                         "fluoroquinolone",
-                         "glycopeptide",
-                         "macrolide",
-                         "tetracycline"),
-         paste0(ab_class, "s"),
-         antibiotics %pm>%
-           subset(group %like% ab_class | 
-                    atc_group1 %like% ab_class | 
-                    atc_group2 %like% ab_class) %pm>%
-          pm_pull(group) %pm>%
-           unique() %pm>%
-           tolower() %pm>%
-           sort() %pm>% 
-           paste(collapse = "/")
-  )
-}
-
-find_ab_names <- function(ab_group, n = 3) {
-  ab_group <- gsub("[^a-zA-Z0-9]", ".*", ab_group)
-  drugs <- antibiotics[which(antibiotics$group %like% ab_group & !antibiotics$ab %like% "[0-9]$"), ]$name
-  paste0(sort(ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
-                      tolower = TRUE, language = NULL)), 
-         collapse = ", ")
-}
--- a/R/first_isolate.R
+++ b/R/first_isolate.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,155 +20,297 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Determine first (weighted) isolates
+#' Determine First (Weighted) Isolates
 #'
-#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
-#' @inheritSection lifecycle Stable lifecycle
-#' @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
+#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. These functions support all four methods as summarised by Hindler *et al.* in 2007 (\doi{10.1086/511864}). To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x a [data.frame] containing isolates. Can be left blank for automatic determination, see *Examples*.
+#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column 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 [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_testcode column name of the test codes. Use `col_testcode = NULL` to **not** exclude certain test codes (such as 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 (`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 `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 `"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 [(ref)](https:/pubmed.ncbi.nlm.nih.gov/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).
-#'
+#' @param col_keyantimicrobials (only useful when `method = "phenotype-based"`) column name of the key antimicrobials to determine first (weighted) isolates, see [key_antimicrobials()]. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' or 'antimicrobials' (case insensitive). Use `col_keyantimicrobials = FALSE` to prevent this. Can also be the output of [key_antimicrobials()].
+#' @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 a [character] vector with test codes that should be excluded (case-insensitive)
+#' @param icu_exclude a [logical] to indicate whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`)
+#' @param specimen_group value in the column set with `col_specimen` to filter on
+#' @param type type to determine weighed isolates; can be `"keyantimicrobials"` or `"points"`, see *Details*
+#' @param method the method to apply, either `"phenotype-based"`, `"episode-based"`, `"patient-based"` or `"isolate-based"` (can be abbreviated), see *Details*. The default is `"phenotype-based"` if antimicrobial test results are present in the data, and `"episode-based"` otherwise.
+#' @param ignore_I [logical] to indicate whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantimicrobials"`, see *Details*
+#' @param points_threshold minimum number of points to require before differences in the antibiogram will lead to inclusion of an isolate when `type = "points"`, see *Details*
+#' @param info a [logical] to indicate info should be printed, defaults to `TRUE` only in interactive mode
+#' @param include_unknown a [logical] to indicate whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
+#' @param include_untested_rsi a [logical] to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use `include_untested_rsi = FALSE` to always return `FALSE` for such rows. This checks the data set for columns of class `<rsi>` and consequently requires transforming columns with antibiotic results using [as.rsi()] first.
+#' @param ... arguments passed on to [first_isolate()] when using [filter_first_isolate()], otherwise arguments passed on to [key_antimicrobials()] (such as `universal`, `gram_negative`, `gram_positive`)
+#' @details 
+#' To conduct epidemiological analyses on antimicrobial resistance data, only so-called first isolates should be included to prevent overestimation and underestimation of antimicrobial resistance. Different methods can be used to do so, see below.
+#' 
+#' These functions are context-aware. This means that the `x` argument can be left blank if used inside a [data.frame] call, see *Examples*.
+#' 
+#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
+#' 
 #' 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 either:
-#' ```
-#'   x[first_isolate(x, ...), ]
-#'   x %>% filter(first_isolate(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", ...)) %>%
-#'     filter(only_weighted_firsts == TRUE) %>%
-#'     select(-only_weighted_firsts, -keyab)
-#' ```
-#' @section 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.
+#' ## Different methods
+#' 
+#' According to Hindler *et al.* (2007, \doi{10.1086/511864}), there are different methods (algorithms) to select first isolates with increasing reliability: isolate-based, patient-based, episode-based and phenotype-based. All methods select on a combination of the taxonomic genus and species (not subspecies). 
+#' 
+#' All mentioned methods are covered in the [first_isolate()] function:
+#' 
+#' 
+#' | **Method**                                       | **Function to apply**                                 |
+#' |--------------------------------------------------|-------------------------------------------------------|
+#' | **Isolate-based**                                | `first_isolate(x, method = "isolate-based")`          |
+#' | *(= all isolates)*                               |                                                       |
+#' |                                                  |                                                       |
+#' |                                                  |                                                       |
+#' | **Patient-based**                                | `first_isolate(x, method = "patient-based")`          |
+#' | *(= first isolate per patient)*                  |                                                       |
+#' |                                                  |                                                       |
+#' |                                                  |                                                       |
+#' | **Episode-based**                                | `first_isolate(x, method = "episode-based")`, or:     |
+#' | *(= first isolate per episode)*                  |                                                       |
+#' | - 7-Day interval from initial isolate            | - `first_isolate(x, method = "e", episode_days = 7)`  |
+#' | - 30-Day interval from initial isolate           | - `first_isolate(x, method = "e", episode_days = 30)` |
+#' |                                                  |                                                       |
+#' |                                                  |                                                       |
+#' | **Phenotype-based**                              | `first_isolate(x, method = "phenotype-based")`, or:   |
+#' | *(= first isolate per phenotype)*                |                                                       |
+#' | - Major difference in any antimicrobial result   | - `first_isolate(x, type = "points")`                 |
+#' | - Any difference in key antimicrobial results    | - `first_isolate(x, type = "keyantimicrobials")`      |
+#' 
+#' ### Isolate-based
+#' 
+#' This method does not require any selection, as all isolates should be included. It does, however, respect all arguments set in the [first_isolate()] function. For example, the default setting for `include_unknown` (`FALSE`) will omit selection of rows without a microbial ID.
+#' 
+#' ### Patient-based
+#' 
+#' To include every genus-species combination per patient once, set the `episode_days` to `Inf`. Although often inappropriate, this method makes sure that no duplicate isolates are selected from the same patient. In a large longitudinal data set, this could mean that isolates are *excluded* that were found years after the initial isolate.
+#' 
+#' ### Episode-based
+#' 
+#' To include every genus-species combination per patient episode once, set the `episode_days` to a sensible number of days. Depending on the type of analysis, this could be 14, 30, 60 or 365. Short episodes are common for analysing specific hospital or ward data, long episodes are common for analysing regional and national data.
+#' 
+#' This is the most common method to correct for duplicate isolates. Patients are categorised into episodes based on their ID and dates (e.g., the date of specimen receipt or laboratory result). While this is a common method, it does not take into account antimicrobial test results. This means that e.g. a methicillin-resistant *Staphylococcus aureus* (MRSA) isolate cannot be differentiated from a wildtype *Staphylococcus aureus* isolate.
+#' 
+#' ### Phenotype-based
+#' 
+#' This is a more reliable method, since it also *weighs* the antibiogram (antimicrobial test results) yielding so-called 'first weighted isolates'. There are two different methods to weigh the antibiogram:
+#' 
+#' 1. Using `type = "points"` and argument `points_threshold`
+#' 
+#'    This method weighs *all* antimicrobial agents available in the data set. Any difference from I to S or R (or vice versa) counts as 0.5 points, a difference from S to R (or vice versa) counts as 1 point. When the sum of points exceeds `points_threshold`, which defaults to `2`, an isolate will be selected as a first weighted isolate.
 #'    
-#' 2. Using `type = "points"` and parameter `points_threshold`
+#'    All antimicrobials are internally selected using the [all_antimicrobials()] function. The output of this function does not need to be passed to the [first_isolate()] function.
 #' 
-#'    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.
+#'       
+#' 2. Using `type = "keyantimicrobials"` and argument `ignore_I`
+#' 
+#'    This method only weighs specific antimicrobial agents, called *key antimicrobials*. Any difference from S to R (or vice versa) in these key antimicrobials will select an isolate as a first weighted isolate. With `ignore_I = FALSE`, also differences from I to S or R (or vice versa) will lead to this. 
+#'    
+#'    Key antimicrobials are internally selected using the [key_antimicrobials()] function, but can also be added manually as a variable to the data and set in the `col_keyantimicrobials` argument. Another option is to pass the output of the [key_antimicrobials()] function directly to the `col_keyantimicrobials` argument.
+#'    
+#'    
+#' The default method is phenotype-based (using `type = "points"`) and episode-based (using `episode_days = 365`). This makes sure that every genus-species combination is selected per patient once per year, while taking into account all antimicrobial test results. If no antimicrobial test results are available in the data set, only the episode-based method is applied at default.
 #' @rdname first_isolate
-#' @seealso [key_antibiotics()]
+#' @seealso [key_antimicrobials()]
 #' @export
 #' @return A [`logical`] vector
 #' @source Methodology of this function is strictly 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/>.
-#' @inheritSection AMR Read more on our website!
+#' - **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/>.
+#' 
+#' - Hindler JF and Stelling J (2007). **Analysis and Presentation of Cumulative Antibiograms: A New Consensus Guideline from the Clinical and Laboratory Standards Institute.** Clinical Infectious Diseases, 44(6), 867–873. \doi{10.1086/511864}
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
-#' # `example_isolates` is a dataset available in the AMR package.
+#' # `example_isolates` is a data set available in the AMR package.
 #' # See ?example_isolates.
 #' 
-#' # basic filtering on first isolates
 #' example_isolates[first_isolate(example_isolates), ]
-#' 
 #' \donttest{
+#' # faster way, only works in R 3.2 and later:
+#' example_isolates[first_isolate(), ]
+#' 
+#' # get all first Gram-negatives
+#' example_isolates[which(first_isolate() & mo_is_gram_negative()), ]
+#'
 #' if (require("dplyr")) {
-#'   # Filter on first isolates:
+#'   # filter on first isolates using dplyr:
 #'   example_isolates %>%
-#'     mutate(first_isolate = first_isolate(.)) %>%
-#'     filter(first_isolate == TRUE)
+#'     filter(first_isolate())
 #'  
-#'   # Short-hand versions:
+#'   # short-hand version:
 #'   example_isolates %>%
 #'     filter_first_isolate()
 #'     
-#'   example_isolates %>%
-#'     filter_first_weighted_isolate()
+#'  # grouped determination of first isolates (also prints group names):
+#'  example_isolates %>%
+#'    group_by(hospital_id) %>%
+#'    mutate(first = first_isolate())
 #'   
-#'   # Now let's see if first isolates matter:
+#'   # now let's see if first isolates matter:
 #'   A <- example_isolates %>%
 #'     group_by(hospital_id) %>%
 #'     summarise(count = n_rsi(GEN),            # gentamicin availability
 #'               resistance = resistance(GEN))  # gentamicin resistance
 #'  
 #'   B <- example_isolates %>%
-#'     filter_first_weighted_isolate() %>%      # the 1st isolate filter
+#'     filter_first_isolate() %>%               # the 1st isolate filter
 #'     group_by(hospital_id) %>%
 #'     summarise(count = n_rsi(GEN),            # gentamicin availability
 #'               resistance = resistance(GEN))  # gentamicin resistance
 #'  
 #'   # Have a look at A and B.
 #'   # B is more reliable because every isolate is counted only once.
-#'   # Gentamicin resistance in hospital D appears to be 3.7% higher than
+#'   # Gentamicin resistance in hospital D appears to be 4.2% higher than
 #'   # when you (erroneously) would have used all isolates for analysis.
 #' }
 #' }
-first_isolate <- function(x,
+first_isolate <- function(x = NULL,
                          col_date = NULL,
                          col_patient_id = NULL,
                          col_mo = NULL,
                          col_testcode = NULL,
                          col_specimen = NULL,
                          col_icu = NULL,
-                          col_keyantibiotics = NULL,
+                          col_keyantimicrobials = NULL,
                          episode_days = 365,
                          testcodes_exclude = NULL,
                          icu_exclude = FALSE,
                          specimen_group = NULL,
-                          type = "keyantibiotics",
+                          type = "points",
+                          method = c("phenotype-based", "episode-based", "patient-based", "isolate-based"),
                          ignore_I = TRUE,
                          points_threshold = 2,
                          info = interactive(),
                          include_unknown = FALSE,
+                          include_untested_rsi = TRUE,
                          ...) {
  
  dots <- unlist(list(...))
  if (length(dots) != 0) {
-    # backwards compatibility with old parameters
-    dots.names <- dots %pm>% names()
+    # backwards compatibility with old arguments
+    dots.names <- names(dots)
    if ("filter_specimen" %in% dots.names) {
      specimen_group <- dots[which(dots.names == "filter_specimen")]
    }
-    if ("tbl" %in% dots.names) {
-      x <- dots[which(dots.names == "tbl")]
+    if ("col_keyantibiotics" %in% dots.names) {
+      col_keyantimicrobials <- dots[which(dots.names == "col_keyantibiotics")]
    }
  }
  
-  stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
-  stop_if(any(dim(x) == 0), "`x` must contain rows and columns")
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
+  meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_testcode, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  if (isFALSE(col_specimen)) {
+    col_specimen <- NULL
+  }
+  meet_criteria(col_specimen, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_icu, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  # method
+  method <- coerce_method(method)
+  meet_criteria(method, allow_class = "character", has_length = 1, is_in = c("phenotype-based", "episode-based", "patient-based", "isolate-based", "p", "e", "i"))
+  # key antimicrobials
+  if (length(col_keyantimicrobials) > 1) {
+    meet_criteria(col_keyantimicrobials, allow_class = "character", has_length = nrow(x))
+    x$keyabcol <- col_keyantimicrobials
+    col_keyantimicrobials <- "keyabcol"
+  } else {
+    if (isFALSE(col_keyantimicrobials)) {
+      col_keyantimicrobials <- NULL
+      # method cannot be phenotype-based anymore
+      if (method == "phenotype-based") {
+        method <- "episode-based"
+      }
+    }
+    meet_criteria(col_keyantimicrobials, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  }
+  meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE)
+  meet_criteria(testcodes_exclude, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(icu_exclude, allow_class = "logical", has_length = 1)
+  meet_criteria(specimen_group, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(type, allow_class = "character", has_length = 1)
+  meet_criteria(ignore_I, allow_class = "logical", has_length = 1)
+  meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(info, allow_class = "logical", has_length = 1)
+  meet_criteria(include_unknown, allow_class = "logical", has_length = 1)
+  meet_criteria(include_untested_rsi, allow_class = "logical", has_length = 1)
  
  # remove data.table, grouping from tibbles, etc.
  x <- as.data.frame(x, stringsAsFactors = FALSE)
  
+  any_col_contains_rsi <- any(vapply(FUN.VALUE = logical(1), 
+                                     X = x, 
+                                     FUN = function(x) any(as.character(x) %in% c("R", "S", "I"), na.rm = TRUE),
+                                     USE.NAMES = FALSE))
+  if (method == "phenotype-based" & !any_col_contains_rsi) {
+    method <- "episode-based"
+  }
+  if (info == TRUE & message_not_thrown_before("first_isolate.method")) {
+    message_(paste0("Determining first isolates using the '", font_bold(method), "' method",
+                    ifelse(method %in% c("episode-based", "phenotype-based"),
+                           ifelse(is.infinite(episode_days),
+                                  " without a specified episode length",
+                                  paste(" and an episode length of", episode_days, "days")),
+                           "")),
+             as_note = FALSE,
+             add_fn = font_black)
+    remember_thrown_message("first_isolate.method")
+  }
+  
  # try to find columns based on type
  # -- mo
  if (is.null(col_mo)) {
-    col_mo <- search_type_in_df(x = x, type = "mo")
+    col_mo <- search_type_in_df(x = x, type = "mo", info = info)
    stop_if(is.null(col_mo), "`col_mo` must be set")
-    stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found")
+  }
+  
+  # methods ----
+  if (method == "isolate-based") {
+    episode_days <- Inf
+    col_keyantimicrobials <- NULL
+    x$dummy_dates <- Sys.Date()
+    col_date <- "dummy_dates"
+    x$dummy_patients <- paste("dummy", seq_len(nrow(x))) # all 'patients' must be unique
+    col_patient_id <- "dummy_patients"
+  } else if (method == "patient-based") {
+    episode_days <- Inf
+    col_keyantimicrobials <- NULL
+  } else if (method == "episode-based") {
+    col_keyantimicrobials <- NULL
+  } else if (method == "phenotype-based") {
+    if (missing(type) & !is.null(col_keyantimicrobials)) {
+      # type = "points" is default, but not set explicitly, while col_keyantimicrobials is
+      type <- "keyantimicrobials"
+    }
+    if (type == "points") {
+      x$keyantimicrobials <- all_antimicrobials(x, only_rsi_columns = FALSE)
+      col_keyantimicrobials <- "keyantimicrobials"
+    } else if (type == "keyantimicrobials" & is.null(col_keyantimicrobials)) {
+      col_keyantimicrobials <- search_type_in_df(x = x, type = "keyantimicrobials", info = info)
+      if (is.null(col_keyantimicrobials)) {
+        # still not found as a column, create it ourselves
+        x$keyantimicrobials <- key_antimicrobials(x, only_rsi_columns = FALSE, col_mo = col_mo, ...)
+        col_keyantimicrobials <- "keyantimicrobials"
+      }
+    }
  }
  
  # -- date
  if (is.null(col_date)) {
-    col_date <- search_type_in_df(x = x, type = "date")
+    col_date <- search_type_in_df(x = x, type = "date", info = info)
    stop_if(is.null(col_date), "`col_date` must be set")
  }
  
@@ -178,34 +320,23 @@ first_isolate <- function(x,
      # WHONET support
      x$patient_id <- paste(x$`First name`, x$`Last name`, x$Sex)
      col_patient_id <- "patient_id"
-      message(font_blue(paste0("NOTE: Using combined columns `", font_bold("First name"), "`, `", font_bold("Last name"), "` and `", font_bold("Sex"), "` as input for `col_patient_id`")))
+      message_("Using combined columns '", font_bold("First name"), "', '", font_bold("Last name"), "' and '", font_bold("Sex"), "' as input for `col_patient_id`")
    } else {
-      col_patient_id <- search_type_in_df(x = x, type = "patient_id")
+      col_patient_id <- search_type_in_df(x = x, type = "patient_id", info = info)
    }
    stop_if(is.null(col_patient_id), "`col_patient_id` must be set")
  }
-  
-  # -- key antibiotics
-  if (is.null(col_keyantibiotics)) {
-    col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
-  }
-  if (isFALSE(col_keyantibiotics)) {
-    col_keyantibiotics <- NULL
-  }
-  
+
  # -- specimen
  if (is.null(col_specimen) & !is.null(specimen_group)) {
-    col_specimen <- search_type_in_df(x = x, type = "specimen")
-  }
-  if (isFALSE(col_specimen)) {
-    col_specimen <- NULL
+    col_specimen <- search_type_in_df(x = x, type = "specimen", info = info)
  }
  
  # check if columns exist
  check_columns_existance <- function(column, tblname = x) {
    if (!is.null(column)) {
      stop_ifnot(column %in% colnames(tblname),
-                 "Column `", column, "` not found.", call = FALSE)
+                 "Column '", column, "' not found.", call = FALSE)
    }
  }
  
@@ -214,7 +345,7 @@ first_isolate <- function(x,
  check_columns_existance(col_mo)
  check_columns_existance(col_testcode)
  check_columns_existance(col_icu)
-  check_columns_existance(col_keyantibiotics)
+  check_columns_existance(col_keyantimicrobials)
  
  # convert dates to Date
  dates <- as.Date(x[, col_date, drop = TRUE])
@@ -223,7 +354,7 @@ first_isolate <- function(x,
  
  # create original row index
  x$newvar_row_index <- seq_len(nrow(x))
-  x$newvar_mo <- x[, col_mo, drop = TRUE]
+  x$newvar_mo <- as.mo(x[, col_mo, drop = TRUE])
  x$newvar_genus_species <- paste(mo_genus(x$newvar_mo), mo_species(x$newvar_mo))
  x$newvar_date <- x[, col_date, drop = TRUE]
  x$newvar_patient_id <- x[, col_patient_id, drop = TRUE]
@@ -232,8 +363,11 @@ first_isolate <- function(x,
    testcodes_exclude <- NULL
  }
  # remove testcodes
-  if (!is.null(testcodes_exclude) & info == TRUE) {
-    message(font_black(paste0("[Criterion] Exclude test codes: ", toString(paste0("'", testcodes_exclude, "'")))))
+  if (!is.null(testcodes_exclude) & info == TRUE & message_not_thrown_before("first_isolate.excludingtestcodes")) {
+    message_("Excluding test codes: ", toString(paste0("'", testcodes_exclude, "'")),
+             add_fn = font_black,
+             as_note = FALSE)
+    remember_thrown_message("first_isolate.excludingtestcodes")
  }
  
  if (is.null(col_specimen)) {
@@ -243,12 +377,15 @@ first_isolate <- function(x,
  # filter on specimen group and keyantibiotics when they are filled in
  if (!is.null(specimen_group)) {
    check_columns_existance(col_specimen, x)
-    if (info == TRUE) {
-      message(font_black(paste0("[Criterion] Exclude other than specimen group '", specimen_group, "'")))
+    if (info == TRUE & message_not_thrown_before("first_isolate.excludingspecimen")) {
+      message_("Excluding other than specimen group '", specimen_group, "'",
+               add_fn = font_black,
+               as_note = FALSE)
+      remember_thrown_message("first_isolate.excludingspecimen")
    }
  }
-  if (!is.null(col_keyantibiotics)) {
-    x$newvar_key_ab <- x[, col_keyantibiotics, drop = TRUE]
+  if (!is.null(col_keyantimicrobials)) {
+    x$newvar_key_ab <- x[, col_keyantimicrobials, drop = TRUE]
  }
  
  if (is.null(testcodes_exclude)) {
@@ -278,13 +415,32 @@ first_isolate <- function(x,
    )
  }
  
-  # no isolates found
+  # speed up - return immediately if obvious
  if (abs(row.start) == Inf | abs(row.end) == Inf) {
    if (info == TRUE) {
-      message(paste("=> Found", font_bold("no isolates")))
+      message_("=> Found ", font_bold("no isolates"),
+               add_fn = font_black, 
+               as_note = FALSE)
    }
    return(rep(FALSE, nrow(x)))
  }
+  if (row.start == row.end) {
+    if (info == TRUE) {
+      message_("=> Found ", font_bold("1 first isolate"), ", as the data only contained 1 row", 
+               add_fn = font_black,
+               as_note = FALSE)
+    }
+    return(TRUE)
+  }
+  if (length(c(row.start:row.end)) == pm_n_distinct(x[c(row.start:row.end), col_mo, drop = TRUE])) {
+    if (info == TRUE) {
+      message_("=> Found ", font_bold(paste(length(c(row.start:row.end)), "first isolates")),
+               ", as all isolates were different microbial species",
+               add_fn = font_black,
+               as_note = FALSE)
+    }
+    return(rep(TRUE, length(c(row.start:row.end))))
+  }
  
  # did find some isolates - add new index numbers of rows
  x$newvar_row_index_sorted <- seq_len(nrow(x))
@@ -292,80 +448,60 @@ first_isolate <- function(x,
  scope.size <- nrow(x[which(x$newvar_row_index_sorted %in% c(row.start + 1:row.end) &
                               !is.na(x$newvar_mo)), , drop = FALSE])
  
-  identify_new_year <- function(x, episode_days) {
-    # I asked on StackOverflow:
-    # https://stackoverflow.com/questions/42122245/filter-one-row-every-year
-    if (length(x) == 1) {
-      return(TRUE)
-    }
-    indices <- integer(0)
-    start <- x[1]
-    ind <- 1
-    indices[ind] <- ind
-    for (i in 2:length(x)) {
-      if (isTRUE(as.numeric(x[i] - start) >= episode_days)) {
-        ind <- ind + 1
-        indices[ind] <- i
-        start <- x[i]
-      }
-    }
-    result <- rep(FALSE, length(x))
-    result[indices] <- TRUE
-    return(result)
-  }
-  
  # Analysis of first isolate ----
  x$other_pat_or_mo <- ifelse(x$newvar_patient_id == pm_lag(x$newvar_patient_id) &
                                x$newvar_genus_species == pm_lag(x$newvar_genus_species),
                              FALSE,
                              TRUE)
  x$episode_group <- paste(x$newvar_patient_id, x$newvar_genus_species)
-  x$more_than_episode_ago <- unlist(lapply(unique(x$episode_group),
-                                           function(g,
-                                                    df = x,
-                                                    days = episode_days) {
-                                             identify_new_year(x = df[which(df$episode_group == g), "newvar_date", drop = TRUE],
-                                                               episode_days = days)
-                                           }))
+  x$more_than_episode_ago <- unlist(lapply(split(x$newvar_date,
+                                                 x$episode_group), 
+                                           is_new_episode,
+                                           episode_days = episode_days),
+                                    use.names = FALSE)
  
  weighted.notice <- ""
-  if (!is.null(col_keyantibiotics)) {
+  if (!is.null(col_keyantimicrobials)) {
    weighted.notice <- "weighted "
-    if (info == TRUE) {
-      if (type == "keyantibiotics") {
-        message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, ",
-                                  ifelse(ignore_I == FALSE, "not ", ""),
-                                  "ignoring I")))
+    if (info == TRUE & message_not_thrown_before("first_isolate.type")) {
+      if (type == "keyantimicrobials") {
+        message_("Basing inclusion on key antimicrobials, ",
+                 ifelse(ignore_I == FALSE, "not ", ""),
+                 "ignoring I",
+                 add_fn = font_black,
+                 as_note = FALSE)
      }
      if (type == "points") {
-        message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, using points threshold of "
-                                  , points_threshold)))
+        message_("Basing inclusion on all antimicrobial results, using a points threshold of "
+                 , points_threshold,
+                 add_fn = font_black,
+                 as_note = FALSE)
      }
+      remember_thrown_message("first_isolate.type")
    }
    type_param <- type
    
-    x$other_key_ab <- !key_antibiotics_equal(y = x$newvar_key_ab,
-                                             z = pm_lag(x$newvar_key_ab),
-                                             type = type_param,
-                                             ignore_I = ignore_I,
-                                             points_threshold = points_threshold,
-                                             info = info)
+    x$other_key_ab <- !antimicrobials_equal(y = x$newvar_key_ab,
+                                            z = pm_lag(x$newvar_key_ab),
+                                            type = type_param,
+                                            ignore_I = ignore_I,
+                                            points_threshold = points_threshold)
    # with key antibiotics
    x$newvar_first_isolate <- pm_if_else(x$newvar_row_index_sorted >= row.start &
-                                        x$newvar_row_index_sorted <= row.end &
-                                        x$newvar_genus_species != "" & 
-                                        (x$other_pat_or_mo | x$more_than_episode_ago | x$other_key_ab),
-                                      TRUE,
-                                      FALSE)
+                                           x$newvar_row_index_sorted <= row.end &
+                                           x$newvar_genus_species != "" & 
+                                           (x$other_pat_or_mo | x$more_than_episode_ago | x$other_key_ab),
+                                         TRUE,
+                                         FALSE)
    
  } else {
    # no key antibiotics
    x$newvar_first_isolate <- pm_if_else(x$newvar_row_index_sorted >= row.start &
-                                        x$newvar_row_index_sorted <= row.end &
-                                        x$newvar_genus_species != "" & 
-                                        (x$other_pat_or_mo | x$more_than_episode_ago),
-                                      TRUE,
-                                      FALSE)
+                                           x$newvar_row_index_sorted <= row.end &
+                                           x$newvar_genus_species != "" & 
+                                           (x$other_pat_or_mo | x$more_than_episode_ago),
+                                         TRUE,
+                                         FALSE)
  }
  
  # first one as TRUE
@@ -376,33 +512,68 @@ first_isolate <- function(x,
  }
  if (!is.null(col_icu)) {
    if (icu_exclude == TRUE) {
-      message(font_black("[Criterion] Exclude isolates from ICU.\n"))
+      message_("Excluding isolates from ICU.",
+               add_fn = font_black,
+               as_note = FALSE)
      x[which(as.logical(x[, col_icu, drop = TRUE])), "newvar_first_isolate"] <- FALSE
    } else {
-      message(font_black("[Criterion] Include isolates from ICU.\n"))
+      message_("Including isolates from ICU.",
+               add_fn = font_black,
+               as_note = FALSE)
    }
  }
  
  decimal.mark <- getOption("OutDec")
  big.mark <- ifelse(decimal.mark != ",", ",", ".")
  
+  if (info == TRUE) {
+    # print group name if used in dplyr::group_by()
+    cur_group <- import_fn("cur_group", "dplyr", error_on_fail = FALSE)
+    if (!is.null(cur_group)) {
+      group_df <- tryCatch(cur_group(), error = function(e) data.frame())
+      if (NCOL(group_df) > 0) {
+        # transform factors to characters
+        group <- vapply(FUN.VALUE = character(1), group_df, function(x) {
+          if (is.numeric(x)) {
+            format(x)
+          } else if (is.logical(x)) {
+            as.character(x)
+          } else {
+            paste0('"', x, '"')
+          }
+        })
+        message_("\nGroup: ", paste0(names(group), " = ", group, collapse = ", "), "\n",
+                 as_note = FALSE,
+                 add_fn = font_red)
+      }
+    }
+  }
+  
  # handle empty microorganisms
  if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) {
-    message(font_blue(paste0("NOTE: ", ifelse(include_unknown == TRUE, "Included ", "Excluded "), 
-                             format(sum(x$newvar_mo == "UNKNOWN", na.rm = TRUE),
-                                    decimal.mark = decimal.mark, big.mark = big.mark), 
-                             " isolates with a microbial ID 'UNKNOWN' (column `", font_bold(col_mo), "`)")))
+    message_(ifelse(include_unknown == TRUE, "Included ", "Excluded "), 
+             format(sum(x$newvar_mo == "UNKNOWN", na.rm = TRUE),
+                    decimal.mark = decimal.mark, big.mark = big.mark), 
+             " isolates with a microbial ID 'UNKNOWN' (in column '", font_bold(col_mo), "')")
  }
  x[which(x$newvar_mo == "UNKNOWN"), "newvar_first_isolate"] <- include_unknown
  
  # exclude all NAs
  if (any(is.na(x$newvar_mo)) & info == TRUE) {
-    message(font_blue(paste0("NOTE: Excluded ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
-                                                       decimal.mark = decimal.mark, big.mark = big.mark), 
-                             " isolates with a microbial ID 'NA' (column `", font_bold(col_mo), "`)")))
+    message_("Excluded ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
+                                 decimal.mark = decimal.mark, big.mark = big.mark), 
+             " isolates with a microbial ID 'NA' (in column '", font_bold(col_mo), "')")
  }
  x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE
  
+  # handle isolates without antibiogram
+  if (include_untested_rsi == FALSE && any(is.rsi(x))) {
+    rsi_all_NA <- which(unname(vapply(FUN.VALUE = logical(1), 
+                                      as.data.frame(t(x[, is.rsi(x), drop = FALSE])),
+                                      function(rsi_values) all(is.na(rsi_values)))))
+    x[rsi_all_NA, "newvar_first_isolate"] <- FALSE
+  }
+  
  # arrange back according to original sorting again
  x <- x[order(x$newvar_row_index), ]
  rownames(x) <- NULL
@@ -411,10 +582,10 @@ first_isolate <- function(x,
    n_found <- sum(x$newvar_first_isolate, na.rm = TRUE)
    p_found_total <- percentage(n_found / nrow(x[which(!is.na(x$newvar_mo)), , drop = FALSE]), digits = 1)
    p_found_scope <- percentage(n_found / scope.size, digits = 1)
-    if (!p_found_total %like% "[.]") {
+    if (p_found_total %unlike% "[.]") {
      p_found_total <- gsub("%", ".0%", p_found_total, fixed = TRUE)
    }
-    if (!p_found_scope %like% "[.]") {
+    if (p_found_scope %unlike% "[.]") {
      p_found_scope <- gsub("%", ".0%", p_found_scope, fixed = TRUE)
    }
    # mark up number of found
@@ -422,13 +593,13 @@ first_isolate <- function(x,
    if (p_found_total != p_found_scope) {
      msg_txt <- paste0("=> Found ",
                        font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
-                        " (", p_found_scope, " within scope and ", p_found_total, " of total where a microbial ID was available)")
+                        " (", method, ", ", p_found_scope, " within scope and ", p_found_total, " of total where a microbial ID was available)")
    } else {
      msg_txt <- paste0("=> Found ",
                        font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
-                        " (", p_found_total, " of total where a microbial ID was available)")
+                        " (", method, ", ", p_found_total, " of total where a microbial ID was available)")
    }
-    message(font_black(msg_txt))
+    message_(msg_txt, add_fn = font_black, as_note = FALSE)
  }
  
  x$newvar_first_isolate
@@ -437,43 +608,43 @@ first_isolate <- function(x,

 #' @rdname first_isolate
 #' @export
-filter_first_isolate <- function(x,
+filter_first_isolate <- function(x = NULL,
                                 col_date = NULL,
                                 col_patient_id = NULL,
                                 col_mo = NULL,
+                                 episode_days = 365,
+                                 method = c("phenotype-based", "episode-based", "patient-based", "isolate-based"),
                                 ...) {
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
+  meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
+  meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE)
+  method <- coerce_method(method)
+  meet_criteria(method, allow_class = "character", has_length = 1, is_in = c("phenotype-based", "episode-based", "patient-based", "isolate-based", "p", "e", "i"))
+  
  subset(x, first_isolate(x = x,
                          col_date = col_date,
                          col_patient_id = col_patient_id,
                          col_mo = col_mo,
+                          episode_days = episode_days,
+                          method = method,
                          ...))
 }

-#' @rdname first_isolate
-#' @export
-filter_first_weighted_isolate <- function(x,
-                                          col_date = NULL,
-                                          col_patient_id = NULL,
-                                          col_mo = NULL,
-                                          col_keyantibiotics = NULL,
-                                          ...) {
-  y <- x
-  if (is.null(col_keyantibiotics)) {
-    # first try to look for it
-    col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
-    # still NULL? Then create it since we are calling filter_first_WEIGHTED_isolate()
-    if (is.null(col_keyantibiotics)) {
-      y$keyab <- suppressMessages(key_antibiotics(x,
-                                                  col_mo = col_mo,
-                                                  ...))
-      col_keyantibiotics <- "keyab"
-    }
+coerce_method <- function(method) {
+  if (is.null(method)) {
+    return(method)
  }
-  
-  subset(x, first_isolate(x = y,
-                          col_date = col_date,
-                          col_patient_id = col_patient_id,
-                          col_mo = col_mo,
-                          col_keyantibiotics = col_keyantibiotics,
-                          ...))
+  method <- tolower(as.character(method[1L]))
+  method[method %like% "^(p$|pheno)"] <- "phenotype-based"
+  method[method %like% "^(e$|episode)"] <- "episode-based"
+  method[method %like% "^patient"] <- "patient-based"
+  method[method %like% "^(i$|iso)"] <- "isolate-based"
+  method
 }
--- a/R/g.test.R
+++ b/R/g.test.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,32 +20,32 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' *G*-test for Count Data
 #'
 #' [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**).
-#' @inheritSection lifecycle Questioning lifecycle
+#' @inheritSection lifecycle Questioning Lifecycle
 #' @inherit stats::chisq.test params return
-#' @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.
+#' @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 `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.
+#' 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 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 (such as 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)
+#' ## *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
+#' ## *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.
@@ -54,7 +54,7 @@
 #'
 #' 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
+#' ## 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.
 #'
 #' 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:
@@ -76,7 +76,7 @@
 #' - 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!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' # = EXAMPLE 1 =
 #' # Shivrain et al. (2006) crossed clearfield rice (which are resistant
@@ -144,7 +144,7 @@ g.test <- function(x,
    DNAME <- paste(paste(DNAME, collapse = "\n"), "and",
                   paste(DNAME2, collapse = "\n"))
  }
-  if (any(x < 0) || anyNA(x))
+  if (any(x < 0) || any(is.na((x)))) # this last one was anyNA, but only introduced in R 3.1.0
    stop("all entries of 'x' must be nonnegative and finite")
  if ((n <- sum(x)) == 0)
    stop("at least one entry of 'x' must be positive")
@@ -200,7 +200,7 @@ g.test <- function(x,
  if (any(E < 5) && is.finite(PARAMETER))
    warning("G-statistic approximation may be incorrect due to E < 5")
  
-  structure(list(statistic = STATISTIC, parameter = PARAMETER,
+  structure(list(statistic = STATISTIC, argument = PARAMETER,
                 p.value = PVAL, method = METHOD, data.name = DNAME,
                 observed = x, expected = E, residuals = (x - E) / sqrt(E),
                 stdres = (x - E) / sqrt(V)), class = "htest")
--- a/R/ggplot_pca.R
+++ b/R/ggplot_pca.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,50 +20,52 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' PCA biplot with `ggplot2`
+#' PCA Biplot with `ggplot2`
 #'
 #' Produces a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
-#' @inheritSection lifecycle Maturing lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param x an object returned by [pca()], [prcomp()] or [princomp()]
 #' @inheritParams stats::biplot.prcomp
 #' @param labels an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].
 #' @param labels_textsize the size of the text used for the labels
 #' @param labels_text_placement adjustment factor the placement of the variable names (`>=1` means further away from the arrow head)
 #' @param groups an optional vector of groups for the labels, with the same length as `labels`. If set, the points and labels will be coloured according to these groups. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].
-#' @param ellipse a logical to indicate whether a normal data ellipse should be drawn for each group (set with `groups`)
+#' @param ellipse a [logical] to indicate whether a normal data ellipse should be drawn for each group (set with `groups`)
 #' @param ellipse_prob statistical size of the ellipse in normal probability
 #' @param ellipse_size the size of the ellipse line
 #' @param ellipse_alpha the alpha (transparency) of the ellipse line
 #' @param points_size the size of the points
 #' @param points_alpha the alpha (transparency) of the points
-#' @param arrows a logical to indicate whether arrows should be drawn
+#' @param arrows a [logical] to indicate whether arrows should be drawn
 #' @param arrows_textsize the size of the text for variable names
 #' @param arrows_colour the colour of the arrow and their text
 #' @param arrows_size the size (thickness) of the arrow lines
 #' @param arrows_textsize the size of the text at the end of the arrows
-#' @param arrows_textangled a logical whether the text at the end of the arrows should be angled
+#' @param arrows_textangled a [logical] whether the text at the end of the arrows should be angled
 #' @param arrows_alpha the alpha (transparency) of the arrows and their text
 #' @param base_textsize the text size for all plot elements except the labels and arrows
-#' @param ... Parameters passed on to functions
+#' @param ... arguments passed on to functions
 #' @source The [ggplot_pca()] function is based on the `ggbiplot()` function from the `ggbiplot` package by Vince Vu, as found on GitHub: <https://github.com/vqv/ggbiplot> (retrieved: 2 March 2020, their latest commit: [`7325e88`](https://github.com/vqv/ggbiplot/commit/7325e880485bea4c07465a0304c470608fffb5d9); 12 February 2015).
 #' 
 #' As per their GPL-2 licence that demands documentation of code changes, the changes made based on the source code were: 
 #' 1. Rewritten code to remove the dependency on packages `plyr`, `scales` and `grid`
 #' 2. Parametrised more options, like arrow and ellipse settings
-#' 3. Added total amount of explained variance as a caption in the plot
-#' 4. Cleaned all syntax based on the `lintr` package and added integrity checks
-#' 5. Updated documentation
-#' @details The colours for labels and points can be changed by adding another scale layer for colour, like `scale_colour_viridis_d()` or `scale_colour_brewer()`.
+#' 3. Hardened all input possibilities by defining the exact type of user input for every argument
+#' 4. Added total amount of explained variance as a caption in the plot
+#' 5. Cleaned all syntax based on the `lintr` package, fixed grammatical errors and added integrity checks
+#' 6. Updated documentation
+#' @details The colours for labels and points can be changed by adding another scale layer for colour, such as `scale_colour_viridis_d()` and `scale_colour_brewer()`.
 #' @rdname ggplot_pca
 #' @export
 #' @examples 
-#' # `example_isolates` is a dataset available in the AMR package.
+#' # `example_isolates` is a data set available in the AMR package.
 #' # See ?example_isolates.
 #'
 #' # See ?pca for more info about Principal Component Analysis (PCA).
+#' \donttest{
 #' if (require("dplyr")) {
 #'   pca_model <- example_isolates %>% 
 #'     filter(mo_genus(mo) == "Staphylococcus") %>% 
@@ -83,9 +85,10 @@
 #'       labs(title = "Title here")
 #'   }
 #' }
+#' }
 ggplot_pca <- function(x,
                       choices = 1:2,
-                       scale = TRUE,
+                       scale = 1,
                       pc.biplot = TRUE,
                       labels = NULL,
                       labels_textsize = 3,
@@ -107,22 +110,27 @@ ggplot_pca <- function(x,
                       ...) {
  
  stop_ifnot_installed("ggplot2")
-  stop_ifnot(length(choices) == 2, "`choices` must be of length 2")
-  stop_ifnot(is.logical(arrows), "`arrows` must be TRUE or FALSE")
-  stop_ifnot(is.logical(arrows_textangled), "`arrows_textangled` must be TRUE or FALSE")
-  stop_ifnot(is.logical(ellipse), "`ellipse` must be TRUE or FALSE")
-  stop_ifnot(is.logical(pc.biplot), "`pc.biplot` must be TRUE or FALSE")
-  stop_ifnot(is.logical(scale), "`scale` must be TRUE or FALSE")
-  stop_ifnot(is.numeric(arrows_alpha), "`arrows_alpha` must be numeric")
-  stop_ifnot(is.numeric(arrows_size), "`arrows_size` must be numeric")
-  stop_ifnot(is.numeric(arrows_textsize), "`arrows_textsize` must be numeric")
-  stop_ifnot(is.numeric(base_textsize), "`base_textsize` must be numeric")
-  stop_ifnot(is.numeric(choices), "`choices` must be numeric")
-  stop_ifnot(is.numeric(ellipse_alpha), "`ellipse_alpha` must be numeric")
-  stop_ifnot(is.numeric(ellipse_prob), "`ellipse_prob` must be numeric")
-  stop_ifnot(is.numeric(ellipse_size), "`ellipse_size` must be numeric")
-  stop_ifnot(is.numeric(labels_text_placement), "`labels_text_placement` must be numeric")
-  stop_ifnot(is.numeric(labels_textsize), "`labels_textsize` must be numeric")
+  meet_criteria(x, allow_class = c("prcomp", "princomp", "PCA", "lda"))
+  meet_criteria(choices, allow_class = c("numeric", "integer"), has_length = 2, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(scale, allow_class = c("numeric", "integer", "logical"), has_length = 1)
+  meet_criteria(pc.biplot, allow_class = "logical", has_length = 1)
+  meet_criteria(labels, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(labels_textsize, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(labels_text_placement, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(groups, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(ellipse, allow_class = "logical", has_length = 1)
+  meet_criteria(ellipse_prob, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(ellipse_size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(ellipse_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(points_size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(points_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(arrows, allow_class = "logical", has_length = 1)
+  meet_criteria(arrows_colour, allow_class = "character", has_length = 1)
+  meet_criteria(arrows_size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(arrows_textsize, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(arrows_textangled, allow_class = "logical", has_length = 1)
+  meet_criteria(arrows_alpha, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(base_textsize, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
  
  calculations <- pca_calculations(pca_model = x,
                                   groups = groups, 
@@ -300,19 +308,20 @@ pca_calculations <- function(pca_model,
    d <- pca_model$svd
    u <- predict(pca_model)$x / nobs.factor
    v <- pca_model$scaling
-    d.total <- sum(d ^ 2)
  } else {
-    stop("Expected a object of class prcomp, princomp, PCA, or lda")
+    stop("Expected an object of class prcomp, princomp, PCA, or lda")
  }
  
  # Scores
  choices <- pmin(choices, ncol(u))
  obs.scale <- 1 - as.integer(scale)
-  df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*"))
+  df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*"),
+                        stringsAsFactors = FALSE)
  
  # Directions
  v <- sweep(v, 2, d ^ as.integer(scale), FUN = "*")
-  df.v <- as.data.frame(v[, choices])
+  df.v <- as.data.frame(v[, choices],
+                        stringsAsFactors = FALSE)
  
  names(df.u) <- c("xvar", "yvar")
  names(df.v) <- names(df.u)
@@ -350,7 +359,8 @@ pca_calculations <- function(pca_model,
      if (nrow(x) <= 2) {
        return(data.frame(X1 = numeric(0),
                          X2 = numeric(0),
-                          groups = character(0)))
+                          groups = character(0),
+                          stringsAsFactors = FALSE))
      }
      sigma <- var(cbind(x$xvar, x$yvar))
      mu <- c(mean(x$xvar), mean(x$yvar))
--- a/R/ggplot_rsi.R
+++ b/R/ggplot_rsi.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,23 +20,24 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' AMR plots with `ggplot2`
+#' AMR Plots with `ggplot2`
 #'
-#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on [ggplot2][ggplot2::ggplot()] functions.
-#' @inheritSection lifecycle Maturing lifecycle
+#' Use these functions to create bar plots for AMR data analysis. All functions rely on [ggplot2][ggplot2::ggplot()] functions.
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param data a [data.frame] with column(s) of class [`rsi`] (see [as.rsi()])
 #' @param position position adjustment of bars, either `"fill"`, `"stack"` or `"dodge"`
 #' @param x variable to show on x axis, either `"antibiotic"` (default) or `"interpretation"` or a grouping variable
 #' @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 `NA` to refer to the existing minimum or maximum
+#' @param breaks a [numeric] vector of positions
+#' @param limits a [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 `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 colours a named vactor with colour to be used for filling. The default colours are colour-blind friendly.
+#' @param aesthetics aesthetics to apply the colours to, defaults to "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"
 #' @param datalabels show datalabels using [labels_rsi_count()]
 #' @param datalabels.size size of the datalabels
 #' @param datalabels.colour colour of the datalabels
@@ -45,27 +46,28 @@
 #' @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 [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()].
+#' @param ... other arguments passed on to [geom_rsi()] or, in case of [scale_rsi_colours()], named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See *Examples*.
+#' @details At default, the names of antibiotics will be shown on the plots using [ab_name()]. This can be set with the `translate_ab` argument. See [count_df()].
 #'
-#' ## The functions
+#' ## 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_y_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_fill_manual()].
+#' [scale_rsi_colours()] sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
 #'
 #' [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.
+#' [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!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
+#' \donttest{
 #' if (require("ggplot2") & require("dplyr")) {
 #'  
 #'   # get antimicrobial results for drugs against a UTI:
@@ -91,7 +93,7 @@
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
 #'     ggplot_rsi(datalabels = FALSE)
 #'  
-#'   # add other ggplot2 parameters as you like:
+#'   # add other ggplot2 arguments as you like:
 #'   example_isolates %>%
 #'     select(AMX, NIT, FOS, TMP, CIP) %>%
 #'     ggplot_rsi(width = 0.5,
@@ -99,47 +101,49 @@
 #'                size = 1,
 #'                linetype = 2,
 #'                alpha = 0.25)
-#'  
+#'
+#'   # you can alter the colours with colour names:
 #'   example_isolates %>%
 #'     select(AMX) %>%
 #'     ggplot_rsi(colours = c(SI = "yellow"))
+#'
+#'   # but you can also use the built-in colour-blind friendly colours for
+#'   # your plots, where "S" is green, "I" is yellow and "R" is red:
+#'   data.frame(x = c("Value1", "Value2", "Value3"),
+#'              y = c(1, 2, 3),
+#'              z = c("Value4", "Value5", "Value6")) %>%
+#'     ggplot() +
+#'     geom_col(aes(x = x, y = y, fill = z)) +
+#'     scale_rsi_colours(Value4 = "S", Value5 = "I", Value6 = "R")
 #'   
+#'   # resistance of ciprofloxacine per age group
+#'   example_isolates %>%
+#'     mutate(first_isolate = first_isolate()) %>%
+#'     filter(first_isolate == TRUE,
+#'            mo == as.mo("E. coli")) %>%
+#'     # age_groups() is also a function in this AMR package:
+#'     group_by(age_group = age_groups(age)) %>%
+#'     select(age_group,
+#'            CIP) %>%
+#'     ggplot_rsi(x = "age_group")
+#'   
+#'   # a shorter version which also adjusts data label colours:
+#'   example_isolates %>%
+#'     select(AMX, NIT, FOS, TMP, CIP) %>%
+#'     ggplot_rsi(colours = FALSE)
+#'
+#'
+#'   # it also supports groups (don't forget to use the group var on `x` or `facet`):
+#'   example_isolates %>%
+#'     select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%
+#'     group_by(hospital_id) %>%
+#'     ggplot_rsi(x = "hospital_id",
+#'                facet = "antibiotic",
+#'                nrow = 1,
+#'                title = "AMR of Anti-UTI Drugs Per Hospital",
+#'                x.title = "Hospital",
+#'                datalabels = FALSE)
 #' }
-#'   
-#' \donttest{
-#' 
-#' # resistance of ciprofloxacine per age group
-#' example_isolates %>%
-#'   mutate(first_isolate = first_isolate(.)) %>%
-#'   filter(first_isolate == TRUE,
-#'          mo == as.mo("E. coli")) %>%
-#'   # `age_groups` is also a function of this AMR package:
-#'   group_by(age_group = age_groups(age)) %>%
-#'   select(age_group,
-#'          CIP) %>%
-#'   ggplot_rsi(x = "age_group")
-#'   
-#' # for colourblind mode, use divergent colours from the viridis package:
-#' example_isolates %>%
-#'   select(AMX, NIT, FOS, TMP, CIP) %>%
-#'   ggplot_rsi() + 
-#'   scale_fill_viridis_d()
-#' # a shorter version which also adjusts data label colours:
-#' example_isolates %>%
-#'   select(AMX, NIT, FOS, TMP, CIP) %>%
-#'   ggplot_rsi(colours = FALSE)
-#'
-#'
-#' # it also supports groups (don't forget to use the group var on `x` or `facet`):
-#' example_isolates %>%
-#'   select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%
-#'   group_by(hospital_id) %>%
-#'   ggplot_rsi(x = "hospital_id",
-#'              facet = "antibiotic",
-#'              nrow = 1,
-#'              title = "AMR of Anti-UTI Drugs Per Hospital",
-#'              x.title = "Hospital",
-#'              datalabels = FALSE)
 #' }
 ggplot_rsi <- function(data,
                       position = NULL,
@@ -155,14 +159,14 @@ ggplot_rsi <- function(data,
                       minimum = 30,
                       language = get_locale(),
                       nrow = NULL,
-                       colours = c(S = "#61a8ff",
-                                   SI = "#61a8ff",
-                                   I = "#61f7ff",
-                                   IR = "#ff6961",
-                                   R = "#ff6961"),
+                       colours = c(S = "#3CAEA3",
+                                   SI = "#3CAEA3",
+                                   I = "#F6D55C",
+                                   IR = "#ED553B",
+                                   R = "#ED553B"),
                       datalabels = TRUE,
                       datalabels.size = 2.5,
-                       datalabels.colour = "gray15",
+                       datalabels.colour = "grey15",
                       title = NULL,
                       subtitle = NULL,
                       caption = NULL,
@@ -171,10 +175,29 @@ ggplot_rsi <- function(data,
                       ...) {
  
  stop_ifnot_installed("ggplot2")
-  
-  x <- x[1]
-  facet <- facet[1]
-  
+  meet_criteria(data, allow_class = "data.frame", contains_column_class = "rsi")
+  meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
+  meet_criteria(x, allow_class = "character", has_length = 1)
+  meet_criteria(fill, allow_class = "character", has_length = 1)
+  meet_criteria(facet, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(breaks, allow_class = c("numeric", "integer"))
+  meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
+  meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(colours, allow_class = c("character", "logical"))
+  meet_criteria(datalabels, allow_class = "logical", has_length = 1)
+  meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(datalabels.colour, allow_class = "character", has_length = 1)
+  meet_criteria(title, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(subtitle, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(caption, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(x.title, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(y.title, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+
  # we work with aes_string later on
  x_deparse <- deparse(substitute(x))
  if (x_deparse != "x") {
@@ -205,11 +228,6 @@ ggplot_rsi <- function(data,
    theme_rsi()
  
  if (fill == "interpretation") {
-    # set RSI colours
-    if (isFALSE(colours) & missing(datalabels.colour)) {
-      # set datalabel colour to middle gray
-      datalabels.colour <- "gray50"
-    }
    p <- p + scale_rsi_colours(colours = colours)
  }
  
@@ -254,9 +272,17 @@ geom_rsi <- function(position = NULL,
                     combine_SI = TRUE,
                     combine_IR = FALSE,
                     ...)  {
-  
+  x <- x[1]
  stop_ifnot_installed("ggplot2")
-  stop_if(is.data.frame(position), "`position` is invalid. Did you accidentally use '%pm>%' instead of '+'?")
+  stop_if(is.data.frame(position), "`position` is invalid. Did you accidentally use '%>%' instead of '+'?")
+  meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
+  meet_criteria(x, allow_class = "character", has_length = 1)
+  meet_criteria(fill, allow_class = "character", has_length = 1)
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
+  meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
  
  y <- "value"
  if (missing(position) | is.null(position)) {
@@ -267,8 +293,6 @@ geom_rsi <- function(position = NULL,
    position <- ggplot2::position_fill(vjust = 0.5, reverse = TRUE)
  }
  
-  x <- x[1]
-  
  # we work with aes_string later on
  x_deparse <- deparse(substitute(x))
  if (x_deparse != "x") {
@@ -284,26 +308,28 @@ geom_rsi <- function(position = NULL,
    x <- "interpretation"
  }
  
-  ggplot2::layer(geom = "bar", stat = "identity", position = position,
-                 mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
-                 params = list(...), data = function(x) {
-                   rsi_df(data = x,
-                          translate_ab = translate_ab,
-                          language = language,
-                          minimum = minimum,
-                          combine_SI = combine_SI,
-                          combine_IR = combine_IR)
-                 })
-  
+  ggplot2::geom_col(
+    data = function(x) {
+      rsi_df(data = x,
+             translate_ab = translate_ab,
+             language = language,
+             minimum = minimum,
+             combine_SI = combine_SI,
+             combine_IR = combine_IR)
+    },
+    mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
+    position = position,
+    ...
+  )
 }

 #' @rdname ggplot_rsi
 #' @export
 facet_rsi <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
-  
-  stop_ifnot_installed("ggplot2")
-  
  facet <- facet[1]
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(facet, allow_class = "character", has_length = 1)
+  meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
  
  # we work with aes_string later on
  facet_deparse <- deparse(substitute(facet))
@@ -327,6 +353,8 @@ facet_rsi <- function(facet = c("interpretation", "antibiotic"), nrow = NULL) {
 #' @export
 scale_y_percent <- function(breaks = seq(0, 1, 0.1), limits = NULL) {
  stop_ifnot_installed("ggplot2")
+  meet_criteria(breaks, allow_class = c("numeric", "integer"))
+  meet_criteria(limits, allow_class = c("numeric", "integer"), has_length = 2, allow_NULL = TRUE, allow_NA = TRUE)
  
  if (all(breaks[breaks != 0] > 1)) {
    breaks <- breaks / 100
@@ -338,24 +366,52 @@ scale_y_percent <- function(breaks = seq(0, 1, 0.1), limits = NULL) {

 #' @rdname ggplot_rsi
 #' @export
-scale_rsi_colours <- function(colours = c(S = "#61a8ff",
-                                          SI = "#61a8ff",
-                                          I = "#61f7ff",
-                                          IR = "#ff6961",
-                                          R = "#ff6961")) {
+scale_rsi_colours <- function(...,
+                              aesthetics = "fill") {
  stop_ifnot_installed("ggplot2")
-  # previous colour: palette = "RdYlGn"
-  # previous colours: values = c("#b22222", "#ae9c20", "#7cfc00")
+  meet_criteria(aesthetics, allow_class = "character", is_in = c("alpha", "colour", "color", "fill", "linetype", "shape", "size"))
  
-  if (!identical(colours, FALSE)) {
-    original_cols <- c(S = "#61a8ff",
-                       SI = "#61a8ff",
-                       I = "#61f7ff",
-                       IR = "#ff6961",
-                       R = "#ff6961")
-    colours <- replace(original_cols, names(colours), colours)
-    ggplot2::scale_fill_manual(values = colours)
+  # behaviour until AMR pkg v1.5.0 and also when coming from ggplot_rsi()
+  if ("colours" %in% names(list(...))) {
+    original_cols <- c(S = "#3CAEA3",
+                       SI = "#3CAEA3",
+                       I = "#F6D55C",
+                       IR = "#ED553B",
+                       R = "#ED553B")
+    colours <- replace(original_cols, names(list(...)$colours), list(...)$colours)
+    return(ggplot2::scale_fill_manual(values = colours))
  }
+  if (identical(unlist(list(...)), FALSE)) {
+    return(invisible())
+  }
+  
+  names_susceptible <- c("S", "SI", "IS", "S+I", "I+S", "susceptible", "Susceptible",
+                         unique(translations_file[which(translations_file$pattern == "Susceptible"),
+                                                  "replacement", drop = TRUE]))
+  names_incr_exposure <- c("I", "intermediate", "increased exposure", "incr. exposure", "Increased exposure", "Incr. exposure",
+                           unique(translations_file[which(translations_file$pattern == "Intermediate"),
+                                                    "replacement", drop = TRUE]),
+                           unique(translations_file[which(translations_file$pattern == "Incr. exposure"),
+                                                    "replacement", drop = TRUE]))
+  names_resistant <- c("R", "IR", "RI", "R+I", "I+R", "resistant", "Resistant",
+                       unique(translations_file[which(translations_file$pattern == "Resistant"), 
+                                                "replacement", drop = TRUE]))
+  
+  susceptible <- rep("#3CAEA3", length(names_susceptible))
+  names(susceptible) <- names_susceptible
+  incr_exposure <- rep("#F6D55C", length(names_incr_exposure))
+  names(incr_exposure) <- names_incr_exposure
+  resistant <- rep("#ED553B", length(names_resistant))
+  names(resistant) <- names_resistant
+  
+  original_cols = c(susceptible, incr_exposure, resistant)
+  dots <- c(...)
+  # replace S, I, R as colours: scale_rsi_colours(mydatavalue = "S")
+  dots[dots == "S"] <- "#3CAEA3"
+  dots[dots == "I"] <- "#F6D55C"
+  dots[dots == "R"] <- "#ED553B"
+  cols <- replace(original_cols, names(dots), dots)
+  ggplot2::scale_discrete_manual(aesthetics = aesthetics, values = cols)
 }

 #' @rdname ggplot_rsi
@@ -381,8 +437,18 @@ labels_rsi_count <- function(position = NULL,
                             combine_SI = TRUE,
                             combine_IR = FALSE,
                             datalabels.size = 3,
-                             datalabels.colour = "gray15") {
+                             datalabels.colour = "grey15") {
  stop_ifnot_installed("ggplot2")
+  meet_criteria(position, allow_class = "character", has_length = 1, is_in = c("fill", "stack", "dodge"), allow_NULL = TRUE)
+  meet_criteria(x, allow_class = "character", has_length = 1)
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
+  meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
+  meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(datalabels.colour, allow_class = "character", has_length = 1)
+  
  if (is.null(position)) {
    position <- "fill"
  }
--- a/R/globals.R
+++ b/R/globals.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,25 +20,69 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

+# add new version numbers here, and add the rules themselves to "data-raw/eucast_rules.tsv" and rsi_translation
+# (sourcing "data-raw/_internals.R" will process the TSV file)
+EUCAST_VERSION_BREAKPOINTS <- list("11.0" = list(version_txt = "v11.0",
+                                                 year = 2021, 
+                                                 title = "'EUCAST Clinical Breakpoint Tables'",
+                                                 url = "https://www.eucast.org/clinical_breakpoints/"),
+                                   "10.0" = list(version_txt = "v10.0",
+                                                 year = 2020, 
+                                                 title = "'EUCAST Clinical Breakpoint Tables'",
+                                                 url = "https://www.eucast.org/ast_of_bacteria/previous_versions_of_documents/"))
+EUCAST_VERSION_EXPERT_RULES <- list("3.1" = list(version_txt = "v3.1",
+                                                 year = 2016, 
+                                                 title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
+                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"),
+                                    "3.2" = list(version_txt = "v3.2",
+                                                 year = 2020, 
+                                                 title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
+                                                 url = "https://www.eucast.org/expert_rules_and_intrinsic_resistance/"))
+
+SNOMED_VERSION <- list(title = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)",
+                       current_source = "US Edition of SNOMED CT from 1 September 2020",
+                       current_version = 12,
+                       current_oid = "2.16.840.1.114222.4.11.1009",
+                       value_set_name = "Microorganism",
+                       url = "https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009")
+
+CATALOGUE_OF_LIFE <- list(
+  year = 2019,
+  version = "Catalogue of Life: {year} Annual Checklist",
+  url_CoL = "http://www.catalogueoflife.org/col/",
+  url_LPSN = "https://lpsn.dsmz.de",
+  yearmonth_LPSN = "March 2021"
+)
+
 globalVariables(c(".rowid",
                  "ab",
                  "ab_txt",
+                  "affect_ab_name",
+                  "affect_mo_name",
                  "angle",
                  "antibiotic",
                  "antibiotics",
                  "atc_group1",
                  "atc_group2",
+                  "base_ab",
                  "code",
+                  "cols",
+                  "count",
                  "data",
+                  "disk",
+                  "dosage",
+                  "dose",
+                  "dose_times",
                  "fullname",
                  "fullname_lower",
                  "g_species",
                  "genus",
                  "gr",
                  "group",
+                  "guideline",
                  "hjust",
                  "input",
                  "intrinsic_resistant",
@@ -46,6 +90,9 @@ globalVariables(c(".rowid",
                  "lang",
                  "language",
                  "lookup",
+                  "method",
+                  "mic",
+                  "mic ",
                  "microorganism",
                  "microorganisms",
                  "microorganisms.codes",
@@ -58,10 +105,12 @@ globalVariables(c(".rowid",
                  "old_name",
                  "pattern",
                  "R",
+                  "reference.rule",
                  "reference.rule_group",
                  "reference.version",
-                  "rsi_translation",
                  "rowid",
+                  "rsi",
+                  "rsi_translation",
                  "rule_group",
                  "rule_name",
                  "se_max",
@@ -70,8 +119,10 @@ globalVariables(c(".rowid",
                  "species_id",
                  "total",
                  "txt",
+                  "type",
                  "value",
                  "varname",
                  "xvar",
+                  "y",
                  "year",
                  "yvar"))
--- a/R/guess_ab_col.R
+++ b/R/guess_ab_col.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,20 +20,21 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Guess antibiotic column
+#' Guess Antibiotic Column
 #'
 #' This tries to find a column name in a data set based on information from the [antibiotics] data set. Also supports WHONET abbreviations.
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 `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.**
+#' @param verbose a [logical] to indicate whether additional info should be printed
+#' @param only_rsi_columns a [logical] to indicate whether only antibiotic columns must be detected that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
+#' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precedence over shorter column names.**
 #' @return A column name of `x`, or `NULL` when no result is found.
 #' @export
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' df <- data.frame(amox = "S",
 #'                  tetr = "R")
@@ -44,7 +45,7 @@
 #' # [1] "tetr"
 #'
 #' guess_ab_col(df, "J01AA07", verbose = TRUE)
-#' # NOTE: Using column `tetr` as input for `J01AA07` (tetracycline).
+#' # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).
 #' # [1] "tetr"
 #'
 #' # WHONET codes
@@ -62,49 +63,34 @@
 #'                  AMP_ED20 = "S")
 #' guess_ab_col(df, "ampicillin")
 #' # [1] "AMP_ED20"
-guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE) {
+guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_rsi_columns = FALSE) {
+  meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
+  meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(verbose, allow_class = "logical", has_length = 1)
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
+  
  if (is.null(x) & is.null(search_string)) {
    return(as.name("guess_ab_col"))
-  }
-  stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
-  
-  if (length(search_string) > 1) {
-    warning("argument 'search_string' has length > 1 and only the first element will be used")
-    search_string <- search_string[1]
-  }
-  search_string <- as.character(search_string)
-  
-  if (search_string %in% colnames(x)) {
-    ab_result <- search_string
  } else {
-    search_string.ab <- suppressWarnings(as.ab(search_string))
-    if (search_string.ab %in% colnames(x)) {
-      ab_result <- colnames(x)[colnames(x) == search_string.ab][1L]
-      
-    } else if (any(tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL))))) {
-      ab_result <- colnames(x)[tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL)))][1L]
-      
-    } else {
-      # sort colnames on length - longest first
-      cols <- colnames(x[, x %pm>% colnames() %pm>% nchar() %pm>% order() %pm>% rev()])
-      df_trans <- data.frame(cols = cols,
-                             abs = suppressWarnings(as.ab(cols)),
-                             stringsAsFactors = FALSE)
-      ab_result <- df_trans[which(df_trans$abs == search_string.ab), "cols"]
-      ab_result <- ab_result[!is.na(ab_result)][1L]
-    }
+    meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = FALSE)
  }
  
+  all_found <- get_column_abx(x, info = verbose, only_rsi_columns = only_rsi_columns, verbose = verbose)
+  search_string.ab <- suppressWarnings(as.ab(search_string))
+  ab_result <- unname(all_found[names(all_found) == search_string.ab])
+  
  if (length(ab_result) == 0) {
    if (verbose == TRUE) {
-      message(paste0("No column found as input for `", search_string,
-                     "` (", ab_name(search_string, language = NULL, tolower = TRUE), ")."))
+      message_("No column found as input for ", search_string,
+               " (", ab_name(search_string, language = NULL, tolower = TRUE), ").",
+               add_fn = font_black,
+               as_note = FALSE)
    }
    return(NULL)
  } else {
    if (verbose == TRUE) {
-      message(font_blue(paste0("NOTE: Using column `", font_bold(ab_result), "` as input for `", search_string,
-                               "` (", ab_name(search_string, language = NULL, tolower = TRUE), ").")))
+      message_("Using column '", font_bold(ab_result), "' as input for ", search_string,
+               " (", ab_name(search_string, language = NULL, tolower = TRUE), ").")
    }
    return(ab_result)
  }
@@ -115,54 +101,80 @@ get_column_abx <- function(x,
                           hard_dependencies = NULL,
                           verbose = FALSE,
                           info = TRUE,
+                           only_rsi_columns = FALSE,
+                           sort = TRUE,
                           ...) {
  
+  # check if retrieved before, then get it from package environment
+  if (identical(unique_call_id(entire_session = FALSE), pkg_env$get_column_abx.call)) {
+    return(pkg_env$get_column_abx.out)
+  }
+  
+  meet_criteria(x, allow_class = "data.frame")
+  meet_criteria(soft_dependencies, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(hard_dependencies, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(verbose, allow_class = "logical", has_length = 1)
+  meet_criteria(info, allow_class = "logical", has_length = 1)
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
+  meet_criteria(sort, allow_class = "logical", has_length = 1)
+  
  if (info == TRUE) {
-    message(font_blue("NOTE: Auto-guessing columns suitable for analysis"), appendLF = FALSE)
+    message_("Auto-guessing columns suitable for analysis", appendLF = FALSE, as_note = FALSE)
  }
  
  x <- as.data.frame(x, stringsAsFactors = FALSE)
+  if (only_rsi_columns == TRUE) {
+    x <- x[, which(is.rsi(x)), drop = FALSE]
+  }
+
  if (NROW(x) > 10000) {
    # only test maximum of 10,000 values per column
    if (info == TRUE) {
-      message(font_blue(paste0(" (using only ", font_bold("the first 10,000 rows"), ")...")), appendLF = FALSE)
+      message_(" (using only ", font_bold("the first 10,000 rows"), ")...",
+               appendLF = FALSE, 
+               as_note = FALSE)
    }
    x <- x[1:10000, , drop = FALSE]
  } else if (info == TRUE) {
-    message(font_blue("..."), appendLF = FALSE)
+    message_("...", appendLF = FALSE, as_note = FALSE)
  }
-  x_bak <- x
+
  # only check columns that are a valid AB code, ATC code, name, abbreviation or synonym,
-  # or already have the rsi class (as.rsi) 
-  # and that have no more than 50% invalid values
-  vectr_antibiotics <- unique(toupper(unlist(antibiotics[, c("ab", "atc", "name", "abbreviations", "synonyms")])))
+  # or already have the <rsi> class (as.rsi) 
+  # and that they have no more than 50% invalid values
+  vectr_antibiotics <- unlist(AB_lookup$generalised_all)
  vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
-  x_columns <- sapply(colnames(x), function(col, df = x_bak) {
-    if (toupper(col) %in% vectr_antibiotics | 
-        is.rsi(as.data.frame(df)[, col, drop = TRUE]) |
-        is.rsi.eligible(as.data.frame(df)[, col, drop = TRUE], threshold = 0.5)) {
-      return(col)
-    } else {
-      return(NA_character_)
-    }
-  })
-  x_columns <- x_columns[!is.na(x_columns)]
-  x <- x[, x_columns, drop = FALSE] # without drop = TRUE, x will become a vector when x_columns is length 1
+  x_columns <- vapply(FUN.VALUE = character(1), 
+                      colnames(x),
+                      function(col, df = x) {
+                        if (generalise_antibiotic_name(col) %in% vectr_antibiotics || 
+                            is.rsi(x[, col, drop = TRUE]) ||
+                            is.rsi.eligible(x[, col, drop = TRUE], threshold = 0.5)
+                        ) {
+                          return(col)
+                        } else {
+                          return(NA_character_)
+                        }
+                      })
  
+  x_columns <- x_columns[!is.na(x_columns)]
+  x <- x[, x_columns, drop = FALSE] # without drop = FALSE, x will become a vector when x_columns is length 1
  df_trans <- data.frame(colnames = colnames(x),
-                         abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)))
+                         abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)),
+                         stringsAsFactors = FALSE)
  df_trans <- df_trans[!is.na(df_trans$abcode), , drop = FALSE]
  x <- as.character(df_trans$colnames)
  names(x) <- df_trans$abcode
  
  # add from self-defined dots (...):
-  # such as get_column_abx(example_isolates %pm>% rename(thisone = AMX), amox = "thisone")
+  # such as get_column_abx(example_isolates %>% rename(thisone = AMX), amox = "thisone")
  dots <- list(...)
  if (length(dots) > 0) {
    newnames <- suppressWarnings(as.ab(names(dots), info = FALSE))
    if (any(is.na(newnames))) {
-      warning("Invalid antibiotic reference(s): ", toString(names(dots)[is.na(newnames)]),
-              call. = FALSE, immediate. = TRUE)
+      warning_("Invalid antibiotic reference(s): ", toString(names(dots)[is.na(newnames)]),
+               call = FALSE,
+               immediate = TRUE)
    }
    # turn all NULLs to NAs
    dots <- unlist(lapply(dots, function(x) if (is.null(x)) NA else x))
@@ -176,38 +188,44 @@ get_column_abx <- function(x,
  
  if (length(x) == 0) {
    if (info == TRUE) {
-      message(font_blue("No columns found."))
+      message_("No columns found.")
    }
+    pkg_env$get_column_abx.call <- unique_call_id(entire_session = FALSE)
+    pkg_env$get_column_abx.out <- x
    return(x)
  }
  
  # sort on name
-  x <- x[order(names(x), x)]
+  if (sort == TRUE) {
+    x <- x[order(names(x), x)]
+  }
  duplicates <- c(x[duplicated(x)], x[duplicated(names(x))]) 
  duplicates <- duplicates[unique(names(duplicates))]
  x <- c(x[!names(x) %in% names(duplicates)], duplicates)
-  x <- x[order(names(x), x)]
+  if (sort == TRUE) {
+    x <- x[order(names(x), x)]
+  }
  
  # succeeded with auto-guessing
  if (info == TRUE) {
-    message(font_blue("OK."))
+    message_(" OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
  }
  
  for (i in seq_len(length(x))) {
    if (info == TRUE & verbose == TRUE & !names(x[i]) %in% names(duplicates)) {
-      message(font_blue(paste0("NOTE: Using column `", font_bold(x[i]), "` as input for `", names(x)[i],
-                               "` (", ab_name(names(x)[i], tolower = TRUE, language = NULL), ").")))
+      message_("Using column '", font_bold(x[i]), "' as input for ", names(x)[i],
+               " (", ab_name(names(x)[i], tolower = TRUE, language = NULL), ").")
    }
    if (info == TRUE & names(x[i]) %in% names(duplicates)) {
-      warning(font_red(paste0("Using column `", font_bold(x[i]), "` as input for `", names(x)[i],
-                              "` (", ab_name(names(x)[i], tolower = TRUE, language = NULL),
-                              "), although it was matched for multiple antibiotics or columns.")), 
-              call. = FALSE, 
-              immediate. = verbose)
+      warning_(paste0("Using column '", font_bold(x[i]), "' as input for ", names(x)[i],
+                      " (", ab_name(names(x)[i], tolower = TRUE, language = NULL),
+                      "), although it was matched for multiple antibiotics or columns."),
+               add_fn = font_red,
+               call = FALSE, 
+               immediate = verbose)
    }
  }
  
-  
  if (!is.null(hard_dependencies)) {
    hard_dependencies <- unique(hard_dependencies)
    if (!all(hard_dependencies %in% names(x))) {
@@ -222,18 +240,16 @@ get_column_abx <- function(x,
    if (info == TRUE & !all(soft_dependencies %in% names(x))) {
      # missing a soft dependency may lower the reliability
      missing <- soft_dependencies[!soft_dependencies %in% names(x)]
-      missing_msg <- paste(paste0(ab_name(missing, tolower = TRUE, language = NULL), 
-                                  " (", missing, ")"), 
-                           collapse = ", ")
-      missing_msg <- paste("NOTE: Reliability would be improved if these antimicrobial results would be available too:",
-                           missing_msg)
-      wrapped <- strwrap(missing_msg,
-                         width = 0.95 * getOption("width"),
-                         exdent = 6)
-      wrapped <- gsub("\\((.*?)\\)", paste0("(", font_bold("\\1"), ")"), wrapped) # add bold abbreviations
-      message(font_blue(wrapped, collapse = "\n"))
+      missing_msg <- vector_and(paste0(ab_name(missing, tolower = TRUE, language = NULL), 
+                                       " (", font_bold(missing, collapse = NULL), ")"), 
+                                quotes = FALSE)
+      message_("Reliability would be improved if these antimicrobial results would be available too: ",
+               missing_msg)
    }
  }
+  
+  pkg_env$get_column_abx.call <- unique_call_id(entire_session = FALSE)
+  pkg_env$get_column_abx.out <- x
  x
 }

@@ -244,8 +260,8 @@ generate_warning_abs_missing <- function(missing, any = FALSE) {
  } else {
    any_txt <- c("", "are")
  }
-  warning(paste0("Introducing NAs since", any_txt[1], " these antimicrobials ", any_txt[2], " required: ",
-                 paste(missing, collapse = ", ")),
-          immediate. = TRUE,
-          call. = FALSE)
+  warning_(paste0("Introducing NAs since", any_txt[1], " these antimicrobials ", any_txt[2], " required: ",
+                  vector_and(missing, quotes = FALSE)),
+           immediate = TRUE,
+           call = FALSE)
 }
--- a/R/italicise_taxonomy.R
+++ b/R/italicise_taxonomy.R
@@ -0,0 +1,132 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' Italicise Taxonomic Families, Genera, Species, Subspecies
+#' 
+#' According to the binomial nomenclature, the lowest four taxonomic levels (family, genus, species, subspecies) should be printed in italic. This function finds taxonomic names within strings and makes them italic.
+#' @inheritSection lifecycle Maturing Lifecycle
+#' @param string a [character] (vector)
+#' @param type type of conversion of the taxonomic names, either "markdown" or "ansi", see *Details*
+#' @details 
+#' This function finds the taxonomic names and makes them italic based on the [microorganisms] data set.
+#' 
+#' The taxonomic names can be italicised using markdown (the default) by adding `*` before and after the taxonomic names, or using ANSI colours by adding `\033[3m` before and `\033[23m` after the taxonomic names. If multiple ANSI colours are not available, no conversion will occur.
+#' 
+#' This function also supports abbreviation of the genus if it is followed by a species, such as "E. coli" and "K. pneumoniae ozaenae".
+#' @inheritSection AMR Read more on Our Website!
+#' @export
+#' @examples
+#' italicise_taxonomy("An overview of Staphylococcus aureus isolates")
+#' italicise_taxonomy("An overview of S. aureus isolates")
+#' 
+#' cat(italicise_taxonomy("An overview of S. aureus isolates", type = "ansi"))
+#' 
+#' # since ggplot2 supports no markdown (yet), use
+#' # italicise_taxonomy() and the `ggtext` pkg for titles:
+#' \donttest{
+#' if (require("ggplot2") && require("ggtext")) {
+#'   ggplot(example_isolates$AMC,
+#'          title = italicise_taxonomy("Amoxi/clav in E. coli")) +
+#'     theme(plot.title = ggtext::element_markdown())
+#' }
+#' }
+italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
+  if (missing(type)) {
+    type <- "markdown"
+  }
+  meet_criteria(string, allow_class = "character")
+  meet_criteria(type, allow_class = "character", has_length = 1, is_in = c("markdown", "ansi"))
+  
+  if (type == "markdown") {
+    before <- "*"
+    after <- "*"
+  } else if (type == "ansi") {
+    if (!has_colour()) {
+      return(string)
+    }
+    before <- "\033[3m"
+    after <- "\033[23m"
+  }
+  
+  vapply(FUN.VALUE = character(1),
+         string,
+         function(s) {
+           s_split <- unlist(strsplit(s, " "))
+           
+           search_strings <- gsub("[^a-zA-Z-]", "", s_split)
+           
+           ind_species <- search_strings != "" &
+             search_strings %in% MO_lookup[which(MO_lookup$rank %in% c("family",
+                                                                       "genus",
+                                                                       "species",
+                                                                       "subspecies",
+                                                                       "infraspecies",
+                                                                       "subsp.")),
+                                           "species",
+                                           drop = TRUE]
+           
+           ind_fullname <- search_strings != "" &
+             search_strings %in% c(MO_lookup[which(MO_lookup$rank %in% c("family",
+                                                                         "genus",
+                                                                         "species",
+                                                                         "subspecies",
+                                                                         "infraspecies",
+                                                                         "subsp.")),
+                                             "fullname",
+                                             drop = TRUE],
+                                   MO_lookup[which(MO_lookup$rank %in% c("family",
+                                                                         "genus",
+                                                                         "species",
+                                                                         "subspecies",
+                                                                         "infraspecies",
+                                                                         "subsp.")),
+                                             "subspecies",
+                                             drop = TRUE])
+
+           # also support E. coli, add "E." to indices
+           has_previous_genera_abbr <- s_split[which(ind_species) - 1] %like_case% "^[A-Z][.]?$"
+           ind_species <- c(which(ind_species), which(ind_species)[has_previous_genera_abbr] - 1)
+           
+           ind <- c(ind_species, which(ind_fullname))
+           
+           s_split[ind] <- paste0(before, s_split[ind], after)
+           s_paste <- paste(s_split, collapse = " ")
+           
+           # clean up a bit
+           s_paste <- gsub(paste0(after, " ", before), " ", s_paste, fixed = TRUE)
+           
+           s_paste
+         },
+         USE.NAMES = FALSE)
+}
+
+#' @rdname italicise_taxonomy
+#' @export
+italicize_taxonomy <- function(string, type = c("markdown", "ansi")) {
+  if (missing(type)) {
+    type <- "markdown"
+  }
+  italicise_taxonomy(string = string, type = type)
+}
--- a/R/join_microorganisms.R
+++ b/R/join_microorganisms.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,28 +20,29 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Join [microorganisms] to a data set
+#' Join [microorganisms] to a Data Set
 #'
-#' Join the data set [microorganisms] easily to an existing table or character vector.
-#' @inheritSection lifecycle Stable lifecycle
+#' Join the data set [microorganisms] easily to an existing data set or to a [character] vector.
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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 [`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$mo` (like `by = "bacteria_id"`), or another column in [microorganisms] (but then it should be named, like `by = c("bacteria_id" = "fullname")`)
-#' @param suffix if there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
-#' @param ... ignored
+#' @param x existing data set to join, or [character] vector. In case of a [character] vector, the resulting [data.frame] will contain a column 'x' with these values.
+#' @param by a variable to join by - if left empty will search for a column with class [`mo`] (created with [as.mo()]) or will be `"mo"` if that column name exists in `x`, could otherwise be a column name of `x` with values that exist in `microorganisms$mo` (such as `by = "bacteria_id"`), or another column in [microorganisms] (but then it should be named, like `by = c("bacteria_id" = "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 ... ignored, only in place to allow future extensions
 #' @details **Note:** As opposed to the `join()` functions of `dplyr`, [character] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. 
 #' 
-#' If the `dplyr` package is installed, their join functions will be used. Otherwise, the much slower [merge()] function from base R will be used.
-#' @inheritSection AMR Read more on our website!
+#' If the `dplyr` package is installed, their join functions will be used. Otherwise, the much slower [merge()] and [interaction()] functions from base R will be used.
+#' @inheritSection AMR Read more on Our Website!
+#' @return a [data.frame]
 #' @export
 #' @examples
 #' left_join_microorganisms(as.mo("K. pneumoniae"))
-#' left_join_microorganisms("B_KLBSL_PNE")
+#' left_join_microorganisms("B_KLBSL_PNMN")
 #'
 #' \donttest{
 #' if (require("dplyr")) {
@@ -61,200 +62,116 @@
 #' }
 #' }
 inner_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  check_dataset_integrity()
-  check_groups_before_join(x, "inner_join_microorganisms")
-  checked <- joins_check_df(x, by)
-  x_class <- get_prejoined_class(x)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_inner <- import_fn("inner_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_inner)) {
-    join <- suppressWarnings(
-      dplyr_inner(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_inner_join(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  }
-  if (NROW(join) > NROW(x)) {
-    warning("The newly joined tbl contains ", nrow(join) - nrow(x), " rows more that its original.")
-  }
-  class(join) <- x_class
-  join
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(suffix, allow_class = "character", has_length = 2)
+  
+  join_microorganisms(type = "inner_join", x = x, by = by, suffix = suffix, ...)
 }

 #' @rdname join
 #' @export
 left_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  check_dataset_integrity()
-  check_groups_before_join(x, "left_join_microorganisms")
-  checked <- joins_check_df(x, by)
-  x_class <- get_prejoined_class(x)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_left <- import_fn("left_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_left)) {
-    join <- suppressWarnings(
-      dplyr_left(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_left_join(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  }
-  if (NROW(join) > NROW(x)) {
-    warning("The newly joined tbl contains ", nrow(join) - nrow(x), " rows more that its original.")
-  }
-  class(join) <- x_class
-  join
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(suffix, allow_class = "character", has_length = 2)
+  
+  join_microorganisms(type = "left_join", x = x, by = by, suffix = suffix, ...)
 }

 #' @rdname join
 #' @export
 right_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  check_dataset_integrity()
-  check_groups_before_join(x, "right_join_microorganisms")
-  checked <- joins_check_df(x, by)
-  x_class <- get_prejoined_class(x)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_right <- import_fn("right_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_right)) {
-    join <- suppressWarnings(
-      dplyr_right(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_right_join(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  }
-  if (NROW(join) > NROW(x)) {
-    warning("The newly joined tbl contains ", nrow(join) - nrow(x), " rows more that its original.")
-  }
-  class(join) <- x_class
-  join
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(suffix, allow_class = "character", has_length = 2)
+  
+  join_microorganisms(type = "right_join", x = x, by = by, suffix = suffix, ...)
 }

 #' @rdname join
 #' @export
 full_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  check_dataset_integrity()
-  check_groups_before_join(x, "full_join_microorganisms")
-  checked <- joins_check_df(x, by)
-  x_class <- get_prejoined_class(x)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_full <- import_fn("full_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_full)) {
-    join <- suppressWarnings(
-      dplyr_full(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_full_join(x = x, y = microorganisms, by = by, suffix = suffix, ...)
-    )
-  }
-  if (NROW(join) > NROW(x)) {
-    warning("The newly joined tbl contains ", nrow(join) - nrow(x), " rows more that its original.")
-  }
-  class(join) <- x_class
-  join
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(suffix, allow_class = "character", has_length = 2)
+  
+  join_microorganisms(type = "full_join", x = x, by = by, suffix = suffix, ...)
 }

 #' @rdname join
 #' @export
 semi_join_microorganisms <- function(x, by = NULL, ...) {
-  check_dataset_integrity()
-  check_groups_before_join(x, "semi_join_microorganisms")
-  x_class <- get_prejoined_class(x)
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_semi <- import_fn("semi_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_semi)) {
-    join <- suppressWarnings(
-      dplyr_semi(x = x, y = microorganisms, by = by, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_semi_join(x = x, y = microorganisms, by = by, ...)
-    )
-  }
-  class(join) <- x_class
-  join
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  
+  join_microorganisms(type = "semi_join", x = x, by = by, ...)
 }

 #' @rdname join
 #' @export
 anti_join_microorganisms <- function(x, by = NULL, ...) {
+  meet_criteria(x, allow_class = c("data.frame", "character"))
+  meet_criteria(by, allow_class = "character", allow_NULL = TRUE)
+  
+  join_microorganisms(type = "anti_join", x = x, by = by, ...)
+}
+
+join_microorganisms <- function(type, x, by, suffix, ...) {
  check_dataset_integrity()
-  check_groups_before_join(x, "anti_join_microorganisms")
-  checked <- joins_check_df(x, by)
-  x_class <- get_prejoined_class(x)
-  x <- checked$x
-  by <- checked$by
-  # use dplyr if available - it's much faster
-  dplyr_anti <- import_fn("anti_join", "dplyr", error_on_fail = FALSE)
-  if (!is.null(dplyr_anti)) {
-    join <- suppressWarnings(
-      dplyr_anti(x = x, y = microorganisms, by = by, ...)
-    )
-  } else {
-    join <- suppressWarnings(
-      pm_anti_join(x = x, y = microorganisms, by = by, ...)
-    )
+  
+  if (!is.data.frame(x)) {
+    x <- data.frame(mo = x, stringsAsFactors = FALSE)
+    by <- "mo"
  }
-  class(join) <- x_class
-  join
-}
-
-joins_check_df <- function(x, by) {
-  if (!any(class(x) %in% c("data.frame", "matrix"))) {
-    x <- data.frame(mo = as.mo(x), stringsAsFactors = FALSE)
-    if (is.null(by)) {
-      by <- "mo"
-    }
-  }
-  x <- as.data.frame(x, stringsAsFactors = FALSE)
  if (is.null(by)) {
-    # search for column with class `mo` and return first one found
-    by <- colnames(x)[lapply(x, is.mo) == TRUE][1]
-    if (is.na(by)) {
-      if ("mo" %in% colnames(x)) {
-        by <- "mo"
-        x[, "mo"] <- as.mo(x[, "mo"])
-      } else {
-        stop("Cannot join - no column found with name 'mo' or with class <mo>.", call. = FALSE)
-      }
+    by <- search_type_in_df(x, "mo", info = FALSE)
+    if (is.null(by) && NCOL(x) == 1) {
+      by <- colnames(x)[1L]
+    } else {
+      stop_if(is.null(by), "no column with microorganism names or codes found, set this column with `by`", call = -2)
    }
-    message('Joining, by = "', by, '"') # message same as dplyr::join functions
+    message_('Joining, by = "', by, '"', add_fn = font_black, as_note = FALSE) # message same as dplyr::join functions
  }
+  if (!all(x[, by, drop = TRUE] %in% MO_lookup$mo, na.rm = TRUE)) {
+    x$join.mo <- as.mo(x[, by, drop = TRUE])
+    by <- c("join.mo" = "mo")
+  } else {
+    x[, by] <- as.mo(x[, by, drop = TRUE])
+  }
+  
  if (is.null(names(by))) {
-    joinby <- colnames(microorganisms)[1]
-    names(joinby) <- by
+    # will always be joined to microorganisms$mo, so add name to that
+    by <- stats::setNames("mo", by)
+  }
+  
+  # use dplyr if available - it's much faster than poorman alternatives
+  dplyr_join <- import_fn(name = type, pkg = "dplyr", error_on_fail = FALSE)
+  if (!is.null(dplyr_join)) {
+    join_fn <- dplyr_join
  } else {
-    joinby <- by
+    # otherwise use poorman, see R/aa_helper_pm_functions.R
+    join_fn <- get(paste0("pm_", type), envir = asNamespace("AMR"))
  }
-  list(x = x,
-       by = joinby)
-}
-
-get_prejoined_class <- function(x) {
-  if (is.data.frame(x)) {
-    class(x)
+  if (type %like% "full|left|right|inner") {
+    joined <- join_fn(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
  } else {
-    "data.frame"
+    joined <- join_fn(x = x, y = AMR::microorganisms, by = by, ...)
  }
-}
-
-check_groups_before_join <- function(x, fn) {
-  if (is.data.frame(x) && !is.null(attributes(x)$groups)) {
-    warning("Groups are dropped, since the ", fn, "() function relies on merge() from base R, not on join() from dplyr.", call. = FALSE)
+  
+  if ("join.mo" %in% colnames(joined)) {
+    if ("mo" %in% colnames(joined)) {
+      ind_mo <- which(colnames(joined) %in% c("mo", "join.mo"))
+      colnames(joined)[ind_mo[1L]] <- paste0("mo", suffix[1L])
+      colnames(joined)[ind_mo[2L]] <- paste0("mo", suffix[2L])
+    } else {
+      colnames(joined)[colnames(joined) == "join.mo"] <- "mo"
+    }
  }
+  
+  if (type %like% "full|left|right|inner" && NROW(joined) > NROW(x)) {
+    warning_("The newly joined data set contains ", nrow(joined) - nrow(x), " rows more than the number of rows of `x`.", call = FALSE)
+  }
+  
+  joined
 }
--- a/R/key_antibiotics.R
+++ b/R/key_antibiotics.R
@@ -1,339 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
-# Developed at the University of Groningen, the Netherlands, in        #
-# collaboration with non-profit organisations Certe Medical            #
-# Diagnostics & Advice, and University Medical Center Groningen.       # 
-#                                                                      #
-# This R package is free software; you can freely use and distribute   #
-# it for both personal and commercial purposes under the terms of the  #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
-# the Free Software Foundation.                                        #
-# We created this package for both routine data analysis and academic  #
-# research and it was publicly released in the hope that it will be    #
-# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
-#                                                                      #
-# Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
-# ==================================================================== #
-
-#' Key antibiotics for 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.
-#' @inheritSection lifecycle Stable lifecycle
-#' @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 **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 [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 (`"."`) by [key_antibiotics()] and ignored by [key_antibiotics_equal()].
-#' 
-#' The [first_isolate()] function only uses this function on the same microbial species from the same patient. Using this, e.g. an MRSA will be included after a susceptible *S. aureus* (MSSA) is found within the same patient episode. Without key antibiotic comparison it would not. See [first_isolate()] for more info.
-#'
-#' 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 **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
-#' @seealso [first_isolate()]
-#' @inheritSection AMR Read more on our website!
-#' @examples
-#' # `example_isolates` is a dataset available in the AMR package.
-#' # See ?example_isolates.
-#' 
-#' # output of the `key_antibiotics` function could be like this:
-#' strainA <- "SSSRR.S.R..S"
-#' strainB <- "SSSIRSSSRSSS"
-#'
-#' # can those strings can be compared with:
-#' key_antibiotics_equal(strainA, strainB)
-#' # TRUE, because I is ignored (as well as missing values)
-#'
-#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
-#' # FALSE, because I is not ignored and so the 4th value differs
-#'
-#' \donttest{
-#' if (require("dplyr")) {
-#'   # set key antibiotics to a new variable
-#'   my_patients <- example_isolates %>%
-#'     mutate(keyab = key_antibiotics(.)) %>%
-#'     mutate(
-#'       # now calculate first isolates
-#'       first_regular = first_isolate(., col_keyantibiotics = FALSE),
-#'       # and first WEIGHTED isolates
-#'       first_weighted = first_isolate(., col_keyantibiotics = "keyab")
-#'     )
-#'  
-#'   # Check the difference, in this data set it results in 7% more isolates:
-#'   sum(my_patients$first_regular, na.rm = TRUE)
-#'   sum(my_patients$first_weighted, na.rm = TRUE)
-#' }
-#' }
-key_antibiotics <- function(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"),
-                            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,
-                            ...) {
-  
-  dots <- unlist(list(...))
-  if (length(dots) != 0) {
-    # backwards compatibility with old parameters
-    dots.names <- dots %pm>% names()
-    if ("info" %in% dots.names) {
-      warnings <- dots[which(dots.names == "info")]
-    }
-  }
-  
-  # try to find columns based on type
-  # -- mo
-  if (is.null(col_mo)) {
-    col_mo <- search_type_in_df(x = x, type = "mo")
-  }
-  stop_if(is.null(col_mo), "`col_mo` must be set")
-  
-  # check columns
-  col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
-                GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
-                GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
-  check_available_columns <- function(x, col.list, warnings = TRUE) {
-    # check columns
-    col.list <- col.list[!is.na(col.list) & !is.null(col.list)]
-    names(col.list) <- col.list
-    col.list.bak <- col.list
-    # are they available as upper case or lower case then?
-    for (i in seq_len(length(col.list))) {
-      if (is.null(col.list[i]) | isTRUE(is.na(col.list[i]))) {
-        col.list[i] <- NA
-      } else if (toupper(col.list[i]) %in% colnames(x)) {
-        col.list[i] <- toupper(col.list[i])
-      } else if (tolower(col.list[i]) %in% colnames(x)) {
-        col.list[i] <- tolower(col.list[i])
-      } else if (!col.list[i] %in% colnames(x)) {
-        col.list[i] <- NA
-      }
-    }
-    if (!all(col.list %in% colnames(x))) {
-      if (warnings == TRUE) {
-        warning("Some columns do not exist and will be ignored: ",
-                col.list.bak[!(col.list %in% colnames(x))] %pm>% toString(),
-                ".\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.",
-                immediate. = TRUE,
-                call. = FALSE)
-      }
-    }
-    col.list
-  }
-  
-  col.list <- check_available_columns(x = x, col.list = col.list, warnings = warnings)
-  universal_1 <- col.list[universal_1]
-  universal_2 <- col.list[universal_2]
-  universal_3 <- col.list[universal_3]
-  universal_4 <- col.list[universal_4]
-  universal_5 <- col.list[universal_5]
-  universal_6 <- col.list[universal_6]
-  GramPos_1 <- col.list[GramPos_1]
-  GramPos_2 <- col.list[GramPos_2]
-  GramPos_3 <- col.list[GramPos_3]
-  GramPos_4 <- col.list[GramPos_4]
-  GramPos_5 <- col.list[GramPos_5]
-  GramPos_6 <- col.list[GramPos_6]
-  GramNeg_1 <- col.list[GramNeg_1]
-  GramNeg_2 <- col.list[GramNeg_2]
-  GramNeg_3 <- col.list[GramNeg_3]
-  GramNeg_4 <- col.list[GramNeg_4]
-  GramNeg_5 <- col.list[GramNeg_5]
-  GramNeg_6 <- col.list[GramNeg_6]
-  
-  universal <- c(universal_1, universal_2, universal_3,
-                 universal_4, universal_5, universal_6)
-  
-  gram_positive <- c(universal,
-                     GramPos_1, GramPos_2, GramPos_3,
-                     GramPos_4, GramPos_5, GramPos_6)
-  gram_positive <- gram_positive[!is.null(gram_positive)]
-  gram_positive <- gram_positive[!is.na(gram_positive)]
-  if (length(gram_positive) < 12) {
-    warning("only using ", length(gram_positive), " different antibiotics as key antibiotics for Gram-positives. See ?key_antibiotics.", call. = FALSE)
-  }
-  
-  gram_negative <- c(universal,
-                     GramNeg_1, GramNeg_2, GramNeg_3,
-                     GramNeg_4, GramNeg_5, GramNeg_6)
-  gram_negative <- gram_negative[!is.null(gram_negative)]
-  gram_negative <- gram_negative[!is.na(gram_negative)]
-  if (length(gram_negative) < 12) {
-    warning("only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics.", call. = FALSE)
-  }
-  
-  x <- as.data.frame(x, stringsAsFactors = FALSE)
-  x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE])
-  x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL)
-  x$key_ab <- NA_character_
-  
-  # Gram +
-  x$key_ab <- pm_if_else(x$gramstain == "Gram-positive",
-                      tryCatch(apply(X = x[, gram_positive],
-                                     MARGIN = 1,
-                                     FUN = function(x) paste(x, collapse = "")),
-                               error = function(e) paste0(rep(".", 12), collapse = "")),
-                      x$key_ab)
-  
-  # Gram -
-  x$key_ab <- pm_if_else(x$gramstain == "Gram-negative",
-                      tryCatch(apply(X = x[, gram_negative],
-                                     MARGIN = 1,
-                                     FUN = function(x) paste(x, collapse = "")),
-                               error = function(e) paste0(rep(".", 12), collapse = "")),
-                      x$key_ab)
-  
-  # format
-  key_abs <- toupper(gsub("[^SIR]", ".", gsub("(NA|NULL)", ".", x$key_ab)))
-  
-  if (pm_n_distinct(key_abs) == 1) {
-    warning("No distinct key antibiotics determined.", call. = FALSE)
-  }
-  
-  key_abs
-  
-}
-
-#' @rdname key_antibiotics
-#' @export
-key_antibiotics_equal <- function(y,
-                                  z,
-                                  type = c("keyantibiotics", "points"),
-                                  ignore_I = TRUE,
-                                  points_threshold = 2,
-                                  info = FALSE) {
-  # y is active row, z is lag
-  x <- y
-  y <- z
-  
-  type <- type[1]
-  
-  stop_ifnot(length(x) == length(y), "length of `x` and `y` must be equal")
-  
-  # only show progress bar on points or when at least 5000 isolates
-  info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
-  
-  result <- logical(length(x))
-  
-  if (info_needed == TRUE) {
-    p <- progress_ticker(length(x))
-    on.exit(close(p))
-  }
-  
-  for (i in seq_len(length(x))) {
-    
-    if (info_needed == TRUE) {
-      p$tick()
-    }
-    
-    if (is.na(x[i])) {
-      x[i] <- ""
-    }
-    if (is.na(y[i])) {
-      y[i] <- ""
-    }
-    
-    if (x[i] == y[i]) {
-      
-      result[i] <- TRUE
-      
-    } else if (nchar(x[i]) != nchar(y[i])) {
-      
-      result[i] <- FALSE
-      
-    } else {
-      
-      x_split <- strsplit(x[i], "")[[1]]
-      y_split <- strsplit(y[i], "")[[1]]
-      
-      if (type == "keyantibiotics") {
-        
-        if (ignore_I == TRUE) {
-          x_split[x_split == "I"] <- "."
-          y_split[y_split == "I"] <- "."
-        }
-        
-        y_split[x_split == "."] <- "."
-        x_split[y_split == "."] <- "."
-        
-        result[i] <- all(x_split == y_split)
-        
-      } else if (type == "points") {
-        # count points for every single character:
-        # - no change is 0 points
-        # - I <-> S|R is 0.5 point
-        # - S|R <-> R|S is 1 point
-        # use the levels of as.rsi (S = 1, I = 2, R = 3)
-        
-        suppressWarnings(x_split <- x_split %pm>% as.rsi() %pm>% as.double())
-        suppressWarnings(y_split <- y_split %pm>% as.rsi() %pm>% as.double())
-        
-        points <- (x_split - y_split) %pm>% abs() %pm>% sum(na.rm = TRUE) / 2
-        result[i] <- points >= points_threshold
-        
-      } else {
-        stop("`", type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?key_antibiotics')
-      }
-    }
-  }
-  if (info_needed == TRUE) {
-    close(p)
-  }
-  result
-}
--- a/R/key_antimicrobials.R
+++ b/R/key_antimicrobials.R
@@ -0,0 +1,325 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' (Key) Antimicrobials for First Weighted Isolates
+#'
+#' These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see [first_isolate()]). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank to determine automatically
+#' @param y,z [character] vectors to compare
+#' @inheritParams first_isolate
+#' @param universal names of **broad-spectrum** antimicrobial agents, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
+#' @param gram_negative names of antibiotic agents for **Gram-positives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
+#' @param gram_positive names of antibiotic agents for **Gram-negatives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
+#' @param antifungal names of antifungal agents for **fungi**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
+#' @param only_rsi_columns a [logical] to indicate whether only columns must be included that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
+#' @param ... ignored, only in place to allow future extensions
+#' @details 
+#' The [key_antimicrobials()] and [all_antimicrobials()] functions are context-aware. This means that the `x` argument can be left blank if used inside a [data.frame] call, see *Examples*.
+#' 
+#' The function [key_antimicrobials()] returns a [character] vector with 12 antimicrobial results for every isolate. The function [all_antimicrobials()] returns a [character] vector with all antimicrobial results for every isolate. These vectors can then be compared using [antimicrobials_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antimicrobials()] and ignored by [antimicrobials_equal()].
+#' 
+#' Please see the [first_isolate()] function how these important functions enable the 'phenotype-based' method for determination of first isolates.
+#'
+#' The default antimicrobial agents used for **all rows** (set in `universal`) are:
+#' 
+#' - Ampicillin
+#' - Amoxicillin/clavulanic acid
+#' - Cefuroxime
+#' - Ciprofloxacin
+#' - Piperacillin/tazobactam
+#' - Trimethoprim/sulfamethoxazole
+#'
+#' The default antimicrobial agents used for **Gram-negative bacteria** (set in `gram_negative`) are:
+#' 
+#' - Cefotaxime
+#' - Ceftazidime
+#' - Colistin
+#' - Gentamicin
+#' - Meropenem
+#' - Tobramycin
+#'
+#' The default antimicrobial agents used for **Gram-positive bacteria** (set in `gram_positive`) are:
+#' 
+#' - Erythromycin
+#' - Oxacillin
+#' - Rifampin
+#' - Teicoplanin
+#' - Tetracycline
+#' - Vancomycin
+#' 
+#' 
+#' The default antimicrobial agents used for **fungi** (set in `antifungal`) are:
+#' 
+#' - Anidulafungin
+#' - Caspofungin
+#' - Fluconazole
+#' - Miconazole
+#' - Nystatin
+#' - Voriconazole
+#' @rdname key_antimicrobials
+#' @export
+#' @seealso [first_isolate()]
+#' @inheritSection AMR Read more on Our Website!
+#' @examples
+#' # `example_isolates` is a data set available in the AMR package.
+#' # See ?example_isolates.
+#' 
+#' # output of the `key_antimicrobials()` function could be like this:
+#' strainA <- "SSSRR.S.R..S"
+#' strainB <- "SSSIRSSSRSSS"
+#'
+#' # those strings can be compared with:
+#' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials")
+#' # TRUE, because I is ignored (as well as missing values)
+#'
+#' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", ignore_I = FALSE)
+#' # FALSE, because I is not ignored and so the 4th [character] differs
+#'
+#' \donttest{
+#' if (require("dplyr")) {
+#'   # set key antibiotics to a new variable
+#'   my_patients <- example_isolates %>%
+#'     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`
+#'     mutate(
+#'       # now calculate first isolates
+#'       first_regular = first_isolate(col_keyantimicrobials = FALSE),
+#'       # and first WEIGHTED isolates
+#'       first_weighted = first_isolate(col_keyantimicrobials = "keyab")
+#'     )
+#'  
+#'   # Check the difference, in this data set it results in more isolates:
+#'   sum(my_patients$first_regular, na.rm = TRUE)
+#'   sum(my_patients$first_weighted, na.rm = TRUE)
+#' }
+#' }
+key_antimicrobials <- function(x = NULL,
+                               col_mo = NULL,
+                               universal = c("ampicillin", "amoxicillin/clavulanic acid", "cefuroxime",
+                                             "piperacillin/tazobactam", "ciprofloxacin", "trimethoprim/sulfamethoxazole"),
+                               gram_negative = c("gentamicin", "tobramycin", "colistin",
+                                                 "cefotaxime", "ceftazidime", "meropenem"),
+                               gram_positive = c("vancomycin", "teicoplanin", "tetracycline",
+                                                 "erythromycin", "oxacillin", "rifampin"),
+                               antifungal = c("anidulafungin", "caspofungin", "fluconazole",
+                                              "miconazole", "nystatin", "voriconazole"),
+                               only_rsi_columns = FALSE,
+                               ...) {
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
+  meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE, is_in = colnames(x))
+  meet_criteria(universal, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(gram_negative, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(gram_positive, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(antifungal, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
+  
+  # force regular [data.frame], not a tibble or data.table
+  x <- as.data.frame(x, stringsAsFactors = FALSE)
+  cols <- get_column_abx(x, info = FALSE, only_rsi_columns = only_rsi_columns)
+  
+  # try to find columns based on type
+  # -- mo
+  if (is.null(col_mo)) {
+    col_mo <- search_type_in_df(x = x, type = "mo", info = FALSE)
+  }
+  if (is.null(col_mo)) {
+    warning_("No column found for `col_mo`, ignoring antibiotics set in `gram_negative` and `gram_positive`, and antimycotics set in `antifungal`", call = FALSE)
+    gramstain <- NA_character_
+    kingdom <- NA_character_
+  } else {
+    x.mo <- as.mo(x[, col_mo, drop = TRUE])
+    gramstain <- mo_gramstain(x.mo, language = NULL)
+    kingdom <- mo_kingdom(x.mo, language = NULL)
+  }
+  
+  AMR_string <- function(x, values, name, filter, cols = cols) {
+    if (is.null(values)) {
+      return(rep(NA_character_, length(which(filter))))
+    }
+    
+    values_old_length <- length(values)
+    values <- as.ab(values, flag_multiple_results = FALSE, info = FALSE)
+    values <- cols[names(cols) %in% values]
+    values_new_length <- length(values)
+    
+    if (values_new_length < values_old_length &
+        any(filter, na.rm = TRUE) &
+        message_not_thrown_before(paste0("key_antimicrobials.", name))) {
+      warning_(ifelse(values_new_length == 0,
+                      "No columns available ",
+                      paste0("Only using ", values_new_length, " out of ", values_old_length, " defined columns ")),
+               "as key antimicrobials for ", name, "s. See ?key_antimicrobials.",
+               call = FALSE)
+      remember_thrown_message(paste0("key_antimicrobials.", name))
+    }
+    
+    generate_antimcrobials_string(x[which(filter), c(universal, values), drop = FALSE])
+  }
+  
+  if (is.null(universal)) {
+    universal <- character(0)
+  } else {
+    universal <- as.ab(universal, flag_multiple_results = FALSE, info = FALSE)
+    universal <- cols[names(cols) %in% universal]
+  }
+  
+  key_ab <- rep(NA_character_, nrow(x))
+  
+  key_ab[which(gramstain == "Gram-negative")] <- AMR_string(x = x,
+                                                            values = gram_negative,
+                                                            name = "Gram-negative",
+                                                            filter = gramstain == "Gram-negative",
+                                                            cols = cols)
+  
+  key_ab[which(gramstain == "Gram-positive")] <- AMR_string(x = x,
+                                                            values = gram_positive,
+                                                            name = "Gram-positive",
+                                                            filter = gramstain == "Gram-positive",
+                                                            cols = cols)
+  
+  key_ab[which(kingdom == "Fungi")] <- AMR_string(x = x,
+                                                  values = antifungal,
+                                                  name = "antifungal",
+                                                  filter = kingdom == "Fungi",
+                                                  cols = cols)
+  
+  # back-up - only use `universal`
+  key_ab[which(is.na(key_ab))] <- AMR_string(x = x,
+                                             values = character(0),
+                                             name = "",
+                                             filter = is.na(key_ab),
+                                             cols = cols)
+  
+  if (length(unique(key_ab)) == 1) {
+    warning_("No distinct key antibiotics determined.", call = FALSE)
+  }
+  
+  key_ab
+}
+
+#' @rdname key_antimicrobials
+#' @export
+all_antimicrobials <- function(x = NULL,
+                               only_rsi_columns = FALSE,
+                               ...) {
+  if (is_null_or_grouped_tbl(x)) {
+    # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
+    # is also fix for using a grouped df as input (a dot as first argument)
+    x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
+  }
+  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
+  meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
+  
+  # force regular [data.frame], not a tibble or data.table
+  x <- as.data.frame(x, stringsAsFactors = FALSE)
+  cols <- get_column_abx(x, only_rsi_columns = only_rsi_columns, info = FALSE, sort = FALSE)
+  
+  generate_antimcrobials_string(x[ , cols, drop = FALSE])
+}
+
+generate_antimcrobials_string <- function(df) {
+  if (NCOL(df) == 0) {
+    return(rep("", NROW(df)))
+  }
+  if (NROW(df) == 0) {
+    return(character(0))
+  }
+  out <- tryCatch(
+    do.call(paste0,
+            lapply(as.list(df),
+                   function(x) {
+                     x <- toupper(as.character(x))
+                     x[!x %in% c("R", "S", "I")] <- "."
+                     paste(x)
+                   })),
+    error = function(e) rep(strrep(".", NCOL(df)), NROW(df)))
+  out
+}
+
+#' @rdname key_antimicrobials
+#' @export
+antimicrobials_equal <- function(y,
+                                  z,
+                                  type = c("points", "keyantimicrobials"),
+                                  ignore_I = TRUE,
+                                  points_threshold = 2,
+                                  ...) {
+  meet_criteria(y, allow_class = "character")
+  meet_criteria(z, allow_class = "character")
+  stop_if(missing(type), "argument \"type\" is missing, with no default")
+  meet_criteria(type, allow_class = "character", has_length = 1, is_in = c("points", "keyantimicrobials"))
+  meet_criteria(ignore_I, allow_class = "logical", has_length = 1)
+  meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  stop_ifnot(length(y) == length(z), "length of `y` and `z` must be equal")
+
+  key2rsi <- function(val) {
+    as.double(as.rsi(gsub(".", NA_character_, unlist(strsplit(val, "")), fixed = TRUE)))
+  }
+  y <- lapply(y, key2rsi)
+  z <- lapply(z, key2rsi)
+  
+  determine_equality <- function(a, b, type, points_threshold, ignore_I) {
+    if (length(a) != length(b)) {
+      # incomparable, so not equal
+      return(FALSE)
+    }
+    # ignore NAs on both sides
+    NA_ind <- which(is.na(a) | is.na(b))
+    a[NA_ind] <- NA_real_
+    b[NA_ind] <- NA_real_
+    
+    if (type == "points") {
+      # count points for every single character:
+      # - no change is 0 points
+      # - I <-> S|R is 0.5 point
+      # - S|R <-> R|S is 1 point
+      # use the levels of as.rsi (S = 1, I = 2, R = 3)
+      # and divide by 2 (S = 0.5, I = 1, R = 1.5)
+      (sum(abs(a - b), na.rm = TRUE) / 2) < points_threshold
+    } else {
+      if (ignore_I == TRUE) {
+        ind <- which(a == 2 | b == 2) # since as.double(as.rsi("I")) == 2
+        a[ind] <- NA_real_
+        b[ind] <- NA_real_
+      }
+      all(a == b, na.rm = TRUE)
+    }
+  }
+  out <- unlist(mapply(FUN = determine_equality,
+                       y,
+                       z,
+                       MoreArgs = list(type = type,
+                                       points_threshold = points_threshold,
+                                       ignore_I = ignore_I),
+                       SIMPLIFY = FALSE,
+                       USE.NAMES = FALSE))
+  out[is.na(y) | is.na(z)] <- NA
+  out
+}
--- a/R/kurtosis.R
+++ b/R/kurtosis.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,21 +20,23 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Kurtosis of the sample
+#' Kurtosis of the Sample
 #'
 #' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. A normal distribution has a kurtosis of 3 and a excess kurtosis of 0.
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param x a vector of values, a [matrix] or a [data.frame]
-#' @param na.rm a logical to indicate whether `NA` values should be stripped before the computation proceeds
-#' @param excess a logical to indicate whether the *excess kurtosis* should be returned, defined as the kurtosis minus 3.
+#' @param na.rm a [logical] to indicate whether `NA` values should be stripped before the computation proceeds
+#' @param excess a [logical] to indicate whether the *excess kurtosis* should be returned, defined as the kurtosis minus 3.
 #' @seealso [skewness()]
 #' @rdname kurtosis
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 kurtosis <- function(x, na.rm = FALSE, excess = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  meet_criteria(excess, allow_class = "logical", has_length = 1)
  UseMethod("kurtosis")
 }

@@ -42,6 +44,8 @@ kurtosis <- function(x, na.rm = FALSE, excess = FALSE) {
 #' @rdname kurtosis
 #' @export
 kurtosis.default <- function(x, na.rm = FALSE, excess = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  meet_criteria(excess, allow_class = "logical", has_length = 1)
  x <- as.vector(x)
  if (na.rm == TRUE) {
    x <- x[!is.na(x)]
@@ -56,6 +60,8 @@ kurtosis.default <- function(x, na.rm = FALSE, excess = FALSE) {
 #' @rdname kurtosis
 #' @export
 kurtosis.matrix <- function(x, na.rm = FALSE, excess = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  meet_criteria(excess, allow_class = "logical", has_length = 1)
  apply(x, 2, kurtosis.default, na.rm = na.rm, excess = excess)
 }

@@ -63,5 +69,7 @@ kurtosis.matrix <- function(x, na.rm = FALSE, excess = FALSE) {
 #' @rdname kurtosis
 #' @export
 kurtosis.data.frame <- function(x, na.rm = FALSE, excess = FALSE) {
-  sapply(x, kurtosis.default, na.rm = na.rm, excess = excess)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  meet_criteria(excess, allow_class = "logical", has_length = 1)
+  vapply(FUN.VALUE = double(1), x, kurtosis.default, na.rm = na.rm, excess = excess)
 }
--- a/R/lifecycle.R
+++ b/R/lifecycle.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,35 +20,35 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 ###############
 # NOTE TO SELF: could also have done this with the 'lifecycle' package, but why add a package dependency for such an easy job??
 ###############

-#' Lifecycles of functions in the `AMR` package
+#' Lifecycles of Functions in the `AMR` Package
 #' @name lifecycle
 #' @rdname lifecycle
-#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://www.Tidyverse.org/lifecycle).
+#' @description Functions in this `AMR` package are categorised using [the lifecycle circle of the Tidyverse as found on www.tidyverse.org/lifecycle](https://lifecycle.r-lib.org/articles/stages.html).
 #' 
 #' \if{html}{\figure{lifecycle_tidyverse.svg}{options: height=200px style=margin-bottom:5px} \cr}
 #' This page contains a section for every lifecycle (with text borrowed from the aforementioned Tidyverse website), so they can be used in the manual pages of the functions. 
-#' @section Experimental lifecycle:
+#' @section Experimental Lifecycle:
 #' \if{html}{\figure{lifecycle_experimental.svg}{options: style=margin-bottom:5px} \cr}
 #' The [lifecycle][AMR::lifecycle] of this function is **experimental**. An experimental function is in early stages of development. The unlying code might be changing frequently. Experimental functions might be removed without deprecation, so you are generally best off waiting until a function is more mature before you use it in production code. Experimental functions are only available in development versions of this `AMR` package and will thus not be included in releases that are submitted to CRAN, since such functions have not yet matured enough.
-#' @section Maturing lifecycle:
+#' @section Maturing Lifecycle:
 #' \if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
 #' The [lifecycle][AMR::lifecycle] of this function is **maturing**. The unlying code of a maturing function has been roughed out, but finer details might still change. Since this function needs wider usage and more extensive testing, you are very welcome [to suggest changes at our repository](https://github.com/msberends/AMR/issues) or [write us an email (see section 'Contact Us')][AMR::AMR].
-#' @section Stable lifecycle:
+#' @section Stable Lifecycle:
 #' \if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
 #' The [lifecycle][AMR::lifecycle] of this function is **stable**. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
 #' 
-#' If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
-#' @section Retired lifecycle:
+#' If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
+#' @section Retired Lifecycle:
 #' \if{html}{\figure{lifecycle_retired.svg}{options: style=margin-bottom:5px} \cr}
 #' The [lifecycle][AMR::lifecycle] of this function is **retired**. A retired function is no longer under active development, and (if appropiate) a better alternative is available. No new arguments will be added, and only the most critical bugs will be fixed. In a future version, this function will be removed.
-#' @section Questioning lifecycle:
+#' @section Questioning Lifecycle:
 #' \if{html}{\figure{lifecycle_questioning.svg}{options: style=margin-bottom:5px} \cr}
 #' The [lifecycle][AMR::lifecycle] of this function is **questioning**. This function might be no longer be optimal approach, or is it questionable whether this function should be in this `AMR` package at all.
 NULL
--- a/R/like.R
+++ b/R/like.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,138 +20,135 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Pattern Matching
+#' Vectorised Pattern Matching with Keyboard Shortcut
 #'
-#' Convenient wrapper around [grep()] to match a pattern: `x %like% pattern`. It always returns a [`logical`] vector and is always case-insensitive (use `x %like_case% pattern` for case-sensitive matching). Also, `pattern` can be as long as `x` to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
-#' @inheritSection lifecycle Stable lifecycle
-#' @param x a character vector where matches are sought, or an object which can be coerced by [as.character()] to a character vector.
-#' @param pattern a character string containing a regular expression (or [character] string for `fixed = TRUE`) to be matched in the given character vector. Coerced by [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.
+#' Convenient wrapper around [grepl()] to match a pattern: `x %like% pattern`. It always returns a [`logical`] vector and is always case-insensitive (use `x %like_case% pattern` for case-sensitive matching). Also, `pattern` can be as long as `x` to compare items of each index in both vectors, or they both can have the same length to iterate over all cases.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x a [character] vector where matches are sought, or an object which can be coerced by [as.character()] to a [character] vector.
+#' @param pattern a [character] vector containing regular expressions (or a [character] string for `fixed = TRUE`) to be matched in the given [character] vector. Coerced by [as.character()] to a [character] string if possible.
 #' @param ignore.case if `FALSE`, the pattern matching is *case sensitive* and if `TRUE`, case is ignored during matching.
-#' @return A [`logical`] vector
+#' @return A [logical] vector
 #' @name like
 #' @rdname like
 #' @export
 #' @details
-#' The `%like%` function:
-#' * Is case-insensitive (use `%like_case%` for case-sensitive matching)
-#' * Supports multiple patterns
-#' * Checks if `pattern` is a regular expression and sets `fixed = TRUE` if not, to greatly improve speed
-#' * Tries again with `perl = TRUE` if regex fails
+#' These [like()] and `%like%`/`%unlike%` functions:
+#' * Are case-insensitive (use `%like_case%`/`%unlike_case%` for case-sensitive matching)
+#' * Support multiple patterns
+#' * Check if `pattern` is a valid regular expression and sets `fixed = TRUE` if not, to greatly improve speed (vectorised over `pattern`)
+#' * Always use compatibility with Perl unless `fixed = TRUE`, to greatly improve speed
 #' 
-#' 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)
-#' @seealso [grep()]
-#' @inheritSection AMR Read more on our website!
+#' Using RStudio? The `%like%`/`%unlike%` functions can also be directly inserted in your code from the Addins menu and can have its own keyboard shortcut like `Shift+Ctrl+L` or `Shift+Cmd+L` (see menu `Tools` > `Modify Keyboard Shortcuts...`). If you keep pressing your shortcut, the inserted text will be iterated over `%like%` -> `%unlike%` -> `%like_case%` -> `%unlike_case%`.
+#' @source Idea from the [`like` function from the `data.table` package](https://github.com/Rdatatable/data.table/blob/ec1259af1bf13fc0c96a1d3f9e84d55d8106a9a4/R/like.R), although altered as explained in *Details*.
+#' @seealso [grepl()]
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
-#' # simple test
 #' a <- "This is a test"
 #' b <- "TEST"
 #' a %like% b
 #' #> TRUE
 #' b %like% a
 #' #> FALSE
-#'
-#' # also supports multiple patterns, length must be equal to x
+#' 
+#' # also supports multiple patterns
 #' a <- c("Test case", "Something different", "Yet another thing")
 #' b <- c(     "case",           "diff",      "yet")
 #' a %like% b
 #' #> TRUE TRUE TRUE
-#'
+#' a %unlike% b
+#' #> FALSE FALSE FALSE
+#' 
+#' a[1] %like% b
+#' #> TRUE FALSE FALSE
+#' a %like% b[1]
+#' #> TRUE FALSE FALSE
+#' 
 #' # get isolates whose name start with 'Ent' or 'ent'
+#' example_isolates[which(mo_name(example_isolates$mo) %like% "^ent"), ]
 #' \donttest{
+#' # faster way, only works in R 3.2 and later:
+#' example_isolates[which(mo_name() %like% "^ent"), ]
+#' 
 #' if (require("dplyr")) {
 #'   example_isolates %>%
-#'     filter(mo_name(mo) %like% "^ent")
+#'     filter(mo_name() %like% "^ent")
 #' }
 #' }
 like <- function(x, pattern, ignore.case = TRUE) {
-  # set to fixed if no regex found
-  fixed <- !any(is_possibly_regex(pattern))
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(pattern, allow_NA = FALSE)
+  meet_criteria(ignore.case, allow_class = "logical", has_length = 1)
+  
+  if (all(is.na(x))) {
+    return(rep(FALSE, length(x)))
+  }
+
+  # set to fixed if no valid regex (vectorised)
+  fixed <- !is_valid_regex(pattern)
+
  if (ignore.case == TRUE) {
-    # set here, otherwise if fixed = TRUE, this warning will be thrown: argument 'ignore.case = TRUE' will be ignored
+    # set here, otherwise if fixed = TRUE, this warning will be thrown: argument `ignore.case = TRUE` will be ignored
    x <- tolower(x)
    pattern <- tolower(pattern)
  }
-  
-  if (length(pattern) > 1 & length(x) == 1) {
-    x <- rep(x, length(pattern))
-  }
-    
-  if (length(pattern) > 1) {
-    res <- vector(length = length(pattern))
-    if (length(x) != length(pattern)) {
-      if (length(x) == 1) {
-        x <- rep(x, length(pattern))
-      }
-      # return TRUE for every 'x' that matches any 'pattern', FALSE otherwise
-      for (i in seq_len(length(res))) {
-        if (is.factor(x[i])) {
-          res[i] <- as.integer(x[i]) %in% grep(pattern[i], levels(x[i]), ignore.case = FALSE, fixed = fixed)
-        } else {
-          res[i] <- grepl(pattern[i], x[i], ignore.case = FALSE, fixed = fixed)
-        }
-      }
-      res <- sapply(pattern, function(pttrn) grepl(pttrn, x, ignore.case = FALSE, fixed = fixed))
-      res2 <- as.logical(rowSums(res))
-      # get only first item of every hit in pattern
-      res2[duplicated(res)] <- FALSE
-      res2[rowSums(res) == 0] <- NA
-      return(res2)
-    } else {
-      # x and pattern are of same length, so items with each other
-      for (i in seq_len(length(res))) {
-        if (is.factor(x[i])) {
-          res[i] <- as.integer(x[i]) %in% grep(pattern[i], levels(x[i]), ignore.case = FALSE, fixed = fixed)
-        } else {
-          res[i] <- grepl(pattern[i], x[i], ignore.case = FALSE, fixed = fixed)
-        }
-      }
-      return(res)
-    }
-  }
-  
-  # the regular way how grepl works; just one pattern against one or more x
+
  if (is.factor(x)) {
-    as.integer(x) %in% grep(pattern, levels(x), ignore.case = FALSE, fixed = fixed)
+    x <- as.character(x)
+  }
+
+  if (length(pattern) == 1) {
+    grepl(pattern, x, ignore.case = FALSE, fixed = fixed, perl = !fixed)
  } else {
-    tryCatch(grepl(pattern, x, ignore.case = FALSE, fixed = fixed),
-             error = function(e) {
-               if (grepl("invalid reg(ular )?exp", e$message, ignore.case = TRUE)) {
-                 # try with perl = TRUE:
-                 return(grepl(pattern = pattern, 
-                                    x = x,
-                                    ignore.case = FALSE, 
-                                    fixed = fixed,
-                                    perl = TRUE))
-               } else {
-                 # stop otherwise
-                 stop(e$message)
-               }
-             })
+    if (length(x) == 1) {
+      x <- rep(x, length(pattern))
+    } else if (length(pattern) != length(x)) {
+      stop_("arguments `x` and `pattern` must be of same length, or either one must be 1 ",
+            "(`x` has length ", length(x), " and `pattern` has length ", length(pattern), ")")
+    }
+    unlist(
+      mapply(FUN = grepl,
+             x = x,
+             pattern = pattern,
+             fixed = fixed,
+             perl = !fixed,
+             MoreArgs = list(ignore.case = FALSE),
+             SIMPLIFY = FALSE,
+             USE.NAMES = FALSE)
+    )
  }
 }

 #' @rdname like
 #' @export
 "%like%" <- function(x, pattern) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(pattern, allow_NA = FALSE)
  like(x, pattern, ignore.case = TRUE)
 }

+#' @rdname like
+#' @export
+"%unlike%" <- function(x, pattern) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(pattern, allow_NA = FALSE)
+  !like(x, pattern, ignore.case = TRUE)
+}
+
 #' @rdname like
 #' @export
 "%like_case%" <- function(x, pattern) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(pattern, allow_NA = FALSE)
  like(x, pattern, ignore.case = FALSE)
 }

-# don't export his one, it's just for convenience in eucast_rules()
-# match all Klebsiella and Raoultella, but not K. aerogenes: fullname %like_perl% "^(Klebsiella(?! aerogenes)|Raoultella)"
-"%like_perl%" <- function(x, pattern) {
-  grepl(x = tolower(x),
-        pattern = tolower(pattern),
-        perl = TRUE,
-        fixed = FALSE,
-        ignore.case = TRUE)
+#' @rdname like
+#' @export
+"%unlike_case%" <- function(x, pattern) {
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(pattern, allow_NA = FALSE)
+  !like(x, pattern, ignore.case = FALSE)
 }
--- a/R/mdro.R
+++ b/R/mdro.R
--- a/R/mic.R
+++ b/R/mic.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,28 +20,71 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Transform input to minimum inhibitory concentrations (MIC)
+#' Transform Input to Minimum Inhibitory Concentrations (MIC)
 #'
-#' This transforms a vector to a new class [`mic`], which is an ordered [factor] with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as `NA` with a warning.
-#' @inheritSection lifecycle Stable lifecycle
+#' This ransforms vectors to a new class [`mic`], which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
+#' @inheritSection lifecycle Stable Lifecycle
 #' @rdname as.mic
-#' @param x vector
-#' @param na.rm a logical indicating whether missing values should be removed
+#' @param x a [character] or [numeric] vector
+#' @param na.rm a [logical] indicating whether missing values should be removed
 #' @details To interpret MIC values as RSI values, use [as.rsi()] on MIC values. It supports guidelines from EUCAST and CLSI.
-#' @return Ordered [factor] with additional class [`mic`]
+#' 
+#' This class for MIC values is a quite a special data type: formally it is an ordered [factor] with valid MIC values as [factor] levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:
+#' 
+#' ```
+#' x <- random_mic(10)
+#' x
+#' #> Class <mic>
+#' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16
+#' 
+#' is.factor(x)
+#' #> [1] TRUE
+#' 
+#' x[1] * 2
+#' #> [1] 32
+#' 
+#' median(x)
+#' #> [1] 26
+#' ```
+#' 
+#' This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering using [numeric] values in data analysis, e.g.:
+#' 
+#' ```
+#' x[x > 4]
+#' #> Class <mic>
+#' #> [1] 16    8     8     64    >=128 32    32    16
+#' 
+#' df <- data.frame(x, hospital = "A")
+#' subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)
+#' #>        x hospital
+#' #> 1     16        A
+#' #> 5     64        A
+#' #> 6  >=128        A
+#' #> 8     32        A
+#' #> 9     32        A
+#' #> 10    16        A
+#' ```
+#' 
+#' The following [generic functions][groupGeneric()] are implemented for the MIC class: `!`, `!=`, `%%`, `%/%`, `&`, `*`, `+`, `-`, `/`, `<`, `<=`, `==`, `>`, `>=`, `^`, `|`, [abs()], [acos()], [acosh()], [all()], [any()], [asin()], [asinh()], [atan()], [atanh()], [ceiling()], [cos()], [cosh()], [cospi()], [cummax()], [cummin()], [cumprod()], [cumsum()], [digamma()], [exp()], [expm1()], [floor()], [gamma()], [lgamma()], [log()], [log1p()], [log2()], [log10()], [max()], [mean()], [min()], [prod()], [range()], [round()], [sign()], [signif()], [sin()], [sinh()], [sinpi()], [sqrt()], [sum()], [tan()], [tanh()], [tanpi()], [trigamma()] and [trunc()]. Some functions of the `stats` package are also implemented: [median()], [quantile()], [mad()], [IQR()], [fivenum()]. Also, [boxplot.stats()] is supported. Since [sd()] and [var()] are non-generic functions, these could not be extended. Use [mad()] as an alternative, or use e.g. `sd(as.numeric(x))` where `x` is your vector of MIC values.
+#' @return Ordered [factor] with additional class [`mic`], that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a [numeric] value.
 #' @aliases mic
 #' @export
 #' @seealso [as.rsi()]
-#' @inheritSection AMR Read more on our website!
+#' @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"))
 #' is.mic(mic_data)
 #'
 #' # this can also coerce combined MIC/RSI values:
 #' as.mic("<=0.002; S") # will return <=0.002
+#' 
+#' # mathematical processing treats MICs as [numeric] values
+#' fivenum(mic_data)
+#' quantile(mic_data)
+#' all(mic_data < 512)
 #'
 #' # interpret MIC values
 #' as.rsi(x = as.mic(2),
@@ -53,13 +96,17 @@
 #'        ab = "AMX",
 #'        guideline = "EUCAST")
 #'
+#' # plot MIC values, see ?plot
 #' plot(mic_data)
-#' barplot(mic_data)
+#' plot(mic_data, mo = "E. coli", ab = "cipro")
 as.mic <- function(x, na.rm = FALSE) {
+  meet_criteria(x, allow_class = c("mic", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  
  if (is.mic(x)) {
    x
  } else {
-    x <- x %pm>% unlist()
+    x <- as.character(unlist(x))
    if (na.rm == TRUE) {
      x <- x[!is.na(x)]
    }
@@ -71,29 +118,29 @@ as.mic <- function(x, na.rm = FALSE) {
    x <- gsub("\u2264", "<=", x, fixed = TRUE)
    x <- gsub("\u2265", ">=", x, fixed = TRUE)
    # remove space between operator and number ("<= 0.002" -> "<=0.002")
-    x <- gsub("(<|=|>) +", "\\1", x)
+    x <- gsub("(<|=|>) +", "\\1", x, perl = TRUE)
    # transform => to >= and =< to <=
    x <- gsub("=<", "<=", x, fixed = TRUE)
    x <- gsub("=>", ">=", x, fixed = TRUE)
    # dots without a leading zero must start with 0
-    x <- gsub("([^0-9]|^)[.]", "\\10.", x)
+    x <- gsub("([^0-9]|^)[.]", "\\10.", x, perl = TRUE)
    # values like "<=0.2560.512" should be 0.512
-    x <- gsub(".*[.].*[.]", "0.", x)
+    x <- gsub(".*[.].*[.]", "0.", x, perl = TRUE)
    # remove ending .0
-    x <- gsub("[.]+0$", "", x)
+    x <- gsub("[.]+0$", "", x, perl = TRUE)
    # remove all after last digit
-    x <- gsub("[^0-9]+$", "", x)
+    x <- gsub("[^0-9]+$", "", x, perl = TRUE)
    # keep only one zero before dot
-    x <- gsub("0+[.]", "0.", x)
+    x <- gsub("0+[.]", "0.", x, perl = TRUE)
    # starting 00 is probably 0.0 if there's no dot yet
-    x[!x %like% "[.]"] <- gsub("^00", "0.0", x[!x %like% "[.]"])
+    x[x %unlike% "[.]"] <- gsub("^00", "0.0", x[!x %like% "[.]"])
    # remove last zeroes
-    x <- gsub("([.].?)0+$", "\\1", x)
-    x <- gsub("(.*[.])0+$", "\\10", x)
+    x <- gsub("([.].?)0+$", "\\1", x, perl = TRUE)
+    x <- gsub("(.*[.])0+$", "\\10", x, perl = TRUE)
    # remove ending .0 again
    x[x %like% "[.]"] <- gsub("0+$", "", x[x %like% "[.]"])
    # never end with dot
-    x <- gsub("[.]$", "", x)
+    x <- gsub("[.]$", "", x, perl = TRUE)
    # force to be character
    x <- as.character(x)
    # trim it
@@ -102,16 +149,16 @@ as.mic <- function(x, na.rm = FALSE) {
    ## previously unempty values now empty - should return a warning later on
    x[x.bak != "" & x == ""] <- "invalid"
    
-    # these are allowed MIC values and will become factor levels
+    # these are allowed MIC values and will become [factor] levels
    ops <- c("<", "<=", "", ">=", ">")
-    lvls <- c(c(t(sapply(ops, function(x) paste0(x, "0.00", 1:9)))),
-              unique(c(t(sapply(ops, function(x) paste0(x, sort(as.double(paste0("0.0", 
+    lvls <- c(c(t(vapply(FUN.VALUE = character(9), ops, function(x) paste0(x, "0.00", 1:9)))),
+              unique(c(t(vapply(FUN.VALUE = character(104), ops, function(x) paste0(x, sort(as.double(paste0("0.0", 
                                                                                 sort(c(1:99, 125, 128, 256, 512, 625)))))))))),
-              unique(c(t(sapply(ops, function(x) paste0(x, sort(as.double(paste0("0.", 
+              unique(c(t(vapply(FUN.VALUE = character(103), ops, function(x) paste0(x, sort(as.double(paste0("0.", 
                                                                                 c(1:99, 125, 128, 256, 512))))))))),
-              c(t(sapply(ops, function(x) paste0(x, sort(c(1:9, 1.5)))))),
-              c(t(sapply(ops, function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])))),
-              c(t(sapply(ops, function(x) paste0(x, sort(c(2 ^ c(7:10), 80 * c(2:12))))))))
+              c(t(vapply(FUN.VALUE = character(10), ops, function(x) paste0(x, sort(c(1:9, 1.5)))))),
+              c(t(vapply(FUN.VALUE = character(45), ops, function(x) paste0(x, c(10:98)[9:98 %% 2 == TRUE])))),
+              c(t(vapply(FUN.VALUE = character(15), ops, function(x) paste0(x, sort(c(2 ^ c(7:10), 80 * c(2:12))))))))
    
    na_before <- x[is.na(x) | x == ""] %pm>% length()
    x[!x %in% lvls] <- NA
@@ -120,23 +167,26 @@ as.mic <- function(x, na.rm = FALSE) {
    if (na_before != na_after) {
      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ""] %pm>%
        unique() %pm>%
-        sort()
-      list_missing <- paste0('"', list_missing, '"', collapse = ", ")
-      warning(na_after - na_before, " results truncated (",
-              round(((na_after - na_before) / length(x)) * 100),
-              "%) that were invalid MICs: ",
-              list_missing, call. = FALSE)
+        sort() %pm>%
+        vector_and(quotes = TRUE)
+      warning_(na_after - na_before, " results truncated (",
+               round(((na_after - na_before) / length(x)) * 100),
+               "%) that were invalid MICs: ",
+               list_missing, call = FALSE)
    }
    
-    structure(.Data = factor(x, levels = lvls, ordered = TRUE),
-              class =  c("mic", "ordered", "factor"))
+    set_clean_class(factor(x, levels = lvls, ordered = TRUE),
+                    new_class =  c("mic", "ordered", "factor"))
  }
 }

 all_valid_mics <- function(x) {
+  if (!inherits(x, c("mic", "character", "factor", "numeric", "integer"))) {
+    return(FALSE)
+  }
  x_mic <- tryCatch(suppressWarnings(as.mic(x[!is.na(x)])),
                    error = function(e) NA)
-  !any(is.na(x_mic)) & !all(is.na(x))
+  !any(is.na(x_mic)) && !all(is.na(x))
 }

 #' @rdname as.mic
@@ -149,37 +199,44 @@ is.mic <- function(x) {
 #' @export
 #' @noRd
 as.double.mic <- function(x, ...) {
-  as.double(gsub("(<|=|>)+", "", as.character(x)))
+  as.double(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }

 #' @method as.integer mic
 #' @export
 #' @noRd
 as.integer.mic <- function(x, ...) {
-  as.integer(gsub("(<|=|>)+", "", as.character(x)))
+  as.integer(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }

 #' @method as.numeric mic
 #' @export
 #' @noRd
 as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub("(<|=|>)+", "", as.character(x)))
+  as.numeric(gsub("[<=>]+", "", as.character(x), perl = TRUE))
 }

 #' @method droplevels mic
 #' @export
 #' @noRd
-droplevels.mic <- function(x, exclude = ifelse(anyNA(levels(x)), NULL, NA), ...) {
+droplevels.mic <- function(x, exclude = if (any(is.na(levels(x)))) NULL else NA, as.mic = TRUE, ...) {
  x <- droplevels.factor(x, exclude = exclude, ...)
-  class(x) <- c("mic", "ordered", "factor")
+  if (as.mic == TRUE) {
+    class(x) <- c("mic", "ordered", "factor")
+  }
  x
 }

 # will be exported using s3_register() in R/zzz.R
 pillar_shaft.mic <- function(x, ...) {
-  out <- trimws(format(x))
+  crude_numbers <- as.double(x)
+  operators <- gsub("[^<=>]+", "", as.character(x))
+  pasted <- trimws(paste0(operators, trimws(format(crude_numbers))))
+  out <- pasted
  out[is.na(x)] <- font_na(NA)
-  create_pillar_column(out, align = "right", min_width = 4)
+  out <- gsub("(<|=|>)", font_silver("\\1"), out)
+  out <- gsub("([.]?0+)$", font_white("\\1"), out)
+  create_pillar_column(out, align = "right", width = max(nchar(pasted)))
 }

 # will be exported using s3_register() in R/zzz.R
@@ -193,60 +250,24 @@ type_sum.mic <- function(x, ...) {
 print.mic <- function(x, ...) {
  cat("Class <mic>\n")
  print(as.character(x), quote = FALSE)
+  att <- attributes(x)
+  if ("na.action" %in% names(att)) {
+    cat(font_silver(paste0("(NA ", class(att$na.action), ": ", paste0(att$na.action, collapse = ", "), ")\n")))
+  }
 }

 #' @method summary mic
 #' @export
 #' @noRd
 summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- length(x)
-  x <- x[!is.na(x)]
-  n <- length(x)
-  value <- c("Class" = "mic",
-             "<NA>" = n_total - n,
-             "Min." = as.character(sort(x)[1]),
-             "Max." = as.character(sort(x)[n]))
-  class(value) <- c("summaryDefault", "table")
-  value
+  summary(as.double(object), ...)
 }

-#' @method plot mic
+#' @method as.matrix mic
 #' @export
-#' @importFrom graphics barplot axis par
-#' @rdname plot
-plot.mic <- function(x,
-                     main = paste("MIC values of", deparse(substitute(x))),
-                     ylab = "Frequency",
-                     xlab = "MIC value",
-                     axes = FALSE,
-                     ...) {
-  barplot(table(droplevels.factor(x)),
-          ylab = ylab,
-          xlab = xlab,
-          axes = axes,
-          main = main,
-          ...)
-  axis(2, seq(0, max(table(droplevels.factor(x)))))
-}
-
-#' @method barplot mic
-#' @export
-#' @importFrom graphics barplot axis
-#' @rdname plot
-barplot.mic <- function(height,
-                        main = paste("MIC values of", deparse(substitute(height))),
-                        ylab = "Frequency",
-                        xlab = "MIC value",
-                        axes = FALSE,
-                        ...) {
-  barplot(table(droplevels.factor(height)),
-          ylab = ylab,
-          xlab = xlab,
-          axes = axes,
-          main = main,
-          ...)
-  axis(2, seq(0, max(table(droplevels.factor(height)))))
+#' @noRd
+as.matrix.mic <- function(x, ...) {
+  as.matrix(as.double(x), ...)
 }

 #' @method [ mic
@@ -286,10 +307,8 @@ barplot.mic <- function(height,
 #' @method c mic
 #' @export
 #' @noRd
-c.mic <- function(x, ...) {
-  y <- unlist(lapply(list(...), as.character))
-  x <- as.character(x)
-  as.mic(c(x, y))
+c.mic <- function(...) {
+  as.mic(unlist(lapply(list(...), as.character)))
 }

 #' @method unique mic
@@ -301,16 +320,447 @@ unique.mic <- function(x, incomparables = FALSE, ...) {
  y
 }

+#' @method sort mic
+#' @export
+#' @noRd
+sort.mic <- function(x, decreasing = FALSE, ...) {
+  if (decreasing == TRUE) {
+    ord <- order(-as.double(x))
+  } else {
+    ord <- order(as.double(x))
+  }
+  x[ord]
+}
+
+#' @method hist mic
+#' @importFrom graphics hist
+#' @export
+#' @noRd
+hist.mic <- function(x, ...) {
+  warning_("Use `plot()` or `ggplot()` for optimal plotting of MIC values", call = FALSE)
+  hist(log2(x))
+}
+
 # will be exported using s3_register() in R/zzz.R
 get_skimmers.mic <- function(column) {
-  sfl <- import_fn("sfl", "skimr", error_on_fail = FALSE)
-  inline_hist <- import_fn("inline_hist", "skimr", error_on_fail = FALSE)
-  sfl(
+  skimr::sfl(
    skim_type = "mic",
-    min = ~as.character(sort(na.omit(.))[1]),
-    max = ~as.character(sort(stats::na.omit(.))[length(stats::na.omit(.))]),
-    median = ~as.character(stats::na.omit(.)[as.double(stats::na.omit(.)) == median(as.double(stats::na.omit(.)))])[1],
-    n_unique = ~pm_n_distinct(., na.rm = TRUE),
-    hist_log2 = ~inline_hist(log2(as.double(stats::na.omit(.))))
+    p0 = ~stats::quantile(., probs = 0, na.rm = TRUE, names = FALSE),
+    p25 = ~stats::quantile(., probs = 0.25, na.rm = TRUE, names = FALSE),
+    p50 = ~stats::quantile(., probs = 0.5, na.rm = TRUE, names = FALSE),
+    p75 = ~stats::quantile(., probs = 0.75, na.rm = TRUE, names = FALSE),
+    p100 = ~stats::quantile(., probs = 1, na.rm = TRUE, names = FALSE),
+    hist = ~skimr::inline_hist(log2(stats::na.omit(.)), 5)
  )
 }
+
+# Miscellaneous mathematical functions ------------------------------------
+
+#' @method mean mic
+#' @export
+#' @noRd
+mean.mic <- function(x, trim = 0, na.rm = FALSE, ...) {
+  mean(as.double(x), trim = trim, na.rm = na.rm, ...)
+}
+
+#' @method median mic
+#' @importFrom stats median
+#' @export
+#' @noRd
+median.mic <- function(x, na.rm = FALSE, ...) {
+  median(as.double(x), na.rm = na.rm, ...)
+}
+
+#' @method quantile mic
+#' @importFrom stats quantile
+#' @export
+#' @noRd
+quantile.mic <- function(x, probs = seq(0, 1, 0.25), na.rm = FALSE,
+                         names = TRUE, type = 7, ...) {
+  quantile(as.double(x), probs = probs, na.rm = na.rm, names = names, type = type, ...)
+}
+
+# Math (see ?groupGeneric) ----------------------------------------------
+
+#' @method abs mic
+#' @export
+#' @noRd
+abs.mic <- function(x) {
+  abs(as.double(x))
+}
+#' @method sign mic
+#' @export
+#' @noRd
+sign.mic <- function(x) {
+  sign(as.double(x))
+}
+#' @method sqrt mic
+#' @export
+#' @noRd
+sqrt.mic <- function(x) {
+  sqrt(as.double(x))
+}
+#' @method floor mic
+#' @export
+#' @noRd
+floor.mic <- function(x) {
+  floor(as.double(x))
+}
+#' @method ceiling mic
+#' @export
+#' @noRd
+ceiling.mic <- function(x) {
+  ceiling(as.double(x))
+}
+#' @method trunc mic
+#' @export
+#' @noRd
+trunc.mic <- function(x, ...) {
+  trunc(as.double(x), ...)
+}
+#' @method round mic
+#' @export
+#' @noRd
+round.mic <- function(x, digits = 0) {
+  round(as.double(x), digits = digits)
+}
+#' @method signif mic
+#' @export
+#' @noRd
+signif.mic <- function(x, digits = 6) {
+  signif(as.double(x), digits = digits)
+}
+#' @method exp mic
+#' @export
+#' @noRd
+exp.mic <- function(x) {
+  exp(as.double(x))
+}
+#' @method log mic
+#' @export
+#' @noRd
+log.mic <- function(x, base = exp(1)) {
+  log(as.double(x), base = base)
+}
+#' @method log10 mic
+#' @export
+#' @noRd
+log10.mic <- function(x) {
+  log10(as.double(x))
+}
+#' @method log2 mic
+#' @export
+#' @noRd
+log2.mic <- function(x) {
+  log2(as.double(x))
+}
+#' @method expm1 mic
+#' @export
+#' @noRd
+expm1.mic <- function(x) {
+  expm1(as.double(x))
+}
+#' @method log1p mic
+#' @export
+#' @noRd
+log1p.mic <- function(x) {
+  log1p(as.double(x))
+}
+#' @method cos mic
+#' @export
+#' @noRd
+cos.mic <- function(x) {
+  cos(as.double(x))
+}
+#' @method sin mic
+#' @export
+#' @noRd
+sin.mic <- function(x) {
+  sin(as.double(x))
+}
+#' @method tan mic
+#' @export
+#' @noRd
+tan.mic <- function(x) {
+  tan(as.double(x))
+}
+#' @method cospi mic
+#' @export
+#' @noRd
+cospi.mic <- function(x) {
+  cospi(as.double(x))
+}
+#' @method sinpi mic
+#' @export
+#' @noRd
+sinpi.mic <- function(x) {
+  sinpi(as.double(x))
+}
+#' @method tanpi mic
+#' @export
+#' @noRd
+tanpi.mic <- function(x) {
+  tanpi(as.double(x))
+}
+#' @method acos mic
+#' @export
+#' @noRd
+acos.mic <- function(x) {
+  acos(as.double(x))
+}
+#' @method asin mic
+#' @export
+#' @noRd
+asin.mic <- function(x) {
+  asin(as.double(x))
+}
+#' @method atan mic
+#' @export
+#' @noRd
+atan.mic <- function(x) {
+  atan(as.double(x))
+}
+#' @method cosh mic
+#' @export
+#' @noRd
+cosh.mic <- function(x) {
+  cosh(as.double(x))
+}
+#' @method sinh mic
+#' @export
+#' @noRd
+sinh.mic <- function(x) {
+  sinh(as.double(x))
+}
+#' @method tanh mic
+#' @export
+#' @noRd
+tanh.mic <- function(x) {
+  tanh(as.double(x))
+}
+#' @method acosh mic
+#' @export
+#' @noRd
+acosh.mic <- function(x) {
+  acosh(as.double(x))
+}
+#' @method asinh mic
+#' @export
+#' @noRd
+asinh.mic <- function(x) {
+  asinh(as.double(x))
+}
+#' @method atanh mic
+#' @export
+#' @noRd
+atanh.mic <- function(x) {
+  atanh(as.double(x))
+}
+#' @method lgamma mic
+#' @export
+#' @noRd
+lgamma.mic <- function(x) {
+  lgamma(as.double(x))
+}
+#' @method gamma mic
+#' @export
+#' @noRd
+gamma.mic <- function(x) {
+  gamma(as.double(x))
+}
+#' @method digamma mic
+#' @export
+#' @noRd
+digamma.mic <- function(x) {
+  digamma(as.double(x))
+}
+#' @method trigamma mic
+#' @export
+#' @noRd
+trigamma.mic <- function(x) {
+  trigamma(as.double(x))
+}
+#' @method cumsum mic
+#' @export
+#' @noRd
+cumsum.mic <- function(x) {
+  cumsum(as.double(x))
+}
+#' @method cumprod mic
+#' @export
+#' @noRd
+cumprod.mic <- function(x) {
+  cumprod(as.double(x))
+}
+#' @method cummax mic
+#' @export
+#' @noRd
+cummax.mic <- function(x) {
+  cummax(as.double(x))
+}
+#' @method cummin mic
+#' @export
+#' @noRd
+cummin.mic <- function(x) {
+  cummin(as.double(x))
+}
+
+# Ops (see ?groupGeneric) -----------------------------------------------
+
+
+#' @method + mic
+#' @export
+#' @noRd
+`+.mic` <- function(e1, e2) {
+  as.double(e1) + as.double(e2)
+}
+
+#' @method - mic
+#' @export
+#' @noRd
+`-.mic` <- function(e1, e2) {
+  as.double(e1) - as.double(e2)
+}
+
+#' @method * mic
+#' @export
+#' @noRd
+`*.mic` <- function(e1, e2) {
+  as.double(e1) * as.double(e2)
+}
+
+#' @method / mic
+#' @export
+#' @noRd
+`/.mic` <- function(e1, e2) {
+  as.double(e1) / as.double(e2)
+}
+
+#' @method ^ mic
+#' @export
+#' @noRd
+`^.mic` <- function(e1, e2) {
+  as.double(e1) ^ as.double(e2)
+}
+
+#' @method %% mic
+#' @export
+#' @noRd
+`%%.mic` <- function(e1, e2) {
+  as.double(e1) %% as.double(e2)
+}
+
+#' @method %/% mic
+#' @export
+#' @noRd
+`%/%.mic` <- function(e1, e2) {
+  as.double(e1) %/% as.double(e2)
+}
+
+#' @method & mic
+#' @export
+#' @noRd
+`&.mic` <- function(e1, e2) {
+  as.double(e1) & as.double(e2)
+}
+
+#' @method | mic
+#' @export
+#' @noRd
+`|.mic` <- function(e1, e2) {
+  as.double(e1) | as.double(e2)
+}
+
+#' @method ! mic
+#' @export
+#' @noRd
+`!.mic` <- function(x) {
+  !as.double(x)
+}
+
+#' @method == mic
+#' @export
+#' @noRd
+`==.mic` <- function(e1, e2) {
+  as.double(e1) == as.double(e2)
+}
+
+#' @method != mic
+#' @export
+#' @noRd
+`!=.mic` <- function(e1, e2) {
+  as.double(e1) != as.double(e2)
+}
+
+#' @method < mic
+#' @export
+#' @noRd
+`<.mic` <- function(e1, e2) {
+  as.double(e1) < as.double(e2)
+}
+
+#' @method <= mic
+#' @export
+#' @noRd
+`<=.mic` <- function(e1, e2) {
+  as.double(e1) <= as.double(e2)
+}
+
+#' @method >= mic
+#' @export
+#' @noRd
+`>=.mic` <- function(e1, e2) {
+  as.double(e1) >= as.double(e2)
+}
+
+#' @method > mic
+#' @export
+#' @noRd
+`>.mic` <- function(e1, e2) {
+  as.double(e1) > as.double(e2)
+}
+
+# Summary (see ?groupGeneric) -------------------------------------------
+
+#' @method all mic
+#' @export
+#' @noRd
+all.mic <- function(..., na.rm = FALSE) {
+  all(as.double(c(...)), na.rm = na.rm)
+}
+#' @method any mic
+#' @export
+#' @noRd
+any.mic <- function(..., na.rm = FALSE) {
+  any(as.double(c(...)), na.rm = na.rm)
+}
+#' @method sum mic
+#' @export
+#' @noRd
+sum.mic <- function(..., na.rm = FALSE) {
+  sum(as.double(c(...)), na.rm = na.rm)
+}
+#' @method prod mic
+#' @export
+#' @noRd
+prod.mic <- function(..., na.rm = FALSE) {
+  prod(as.double(c(...)), na.rm = na.rm)
+}
+#' @method min mic
+#' @export
+#' @noRd
+min.mic <- function(..., na.rm = FALSE) {
+  min(as.double(c(...)), na.rm = na.rm)
+}
+#' @method max mic
+#' @export
+#' @noRd
+max.mic <- function(..., na.rm = FALSE) {
+  max(as.double(c(...)), na.rm = na.rm)
+}
+#' @method range mic
+#' @export
+#' @noRd
+range.mic <- function(..., na.rm = FALSE) {
+  range(as.double(c(...)), na.rm = na.rm)
+}
--- a/R/mo.R
+++ b/R/mo.R
--- a/R/mo_matching_score.R
+++ b/R/mo_matching_score.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,32 +20,36 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Calculate the matching score for microorganisms
+#' Calculate the Matching Score for Microorganisms
 #' 
-#' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input. 
+#' This algorithm is used by [as.mo()] and all the [`mo_*`][mo_property()] functions to determine the most probable match of taxonomic records based on user input. 
+#' @inheritSection lifecycle Stable Lifecycle
+#' @author Matthijs S. Berends
 #' @param x Any user input value(s)
 #' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
-#' @section Matching score for microorganisms:
+#' @section Matching Score for Microorganisms:
 #' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, is calculated as:
 #' 
-#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
+#' \ifelse{latex}{\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}}{\ifelse{html}{\figure{mo_matching_score.png}{options: width="300px" alt="mo matching score"}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}}
 #' 
 #' where:
 #' 
-#' * \eqn{x} is the user input;
-#' * \eqn{n} is a taxonomic name (genus, species, and subspecies);
-#' * \eqn{l_n}{l_n} is the length of \eqn{n};
-#' * lev is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance), which counts any insertion, deletion and substitution as 1 that is needed to change \eqn{x} into \eqn{n};
-#' * \eqn{p_n}{p_n} is the human pathogenic prevalence group of \eqn{n}, as described below;
-#' * \eqn{k_n}{p_n} is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
+#' * \ifelse{html}{\out{<i>x</i> is the user input;}}{\eqn{x} is the user input;}
+#' * \ifelse{html}{\out{<i>n</i> is a taxonomic name (genus, species, and subspecies);}}{\eqn{n} is a taxonomic name (genus, species, and subspecies);}
+#' * \ifelse{html}{\out{<i>l<sub>n</sub></i> is the length of <i>n</i>;}}{l_n is the length of \eqn{n};}
+#' * \ifelse{html}{\out{<i>lev</i> is the <a href="https://en.wikipedia.org/wiki/Levenshtein_distance">Levenshtein distance function</a>, which counts any insertion, deletion and substitution as 1 that is needed to change <i>x</i> into <i>n</i>;}}{lev is the Levenshtein distance function, which counts any insertion, deletion and substitution as 1 that is needed to change \eqn{x} into \eqn{n};}
+#' * \ifelse{html}{\out{<i>p<sub>n</sub></i> is the human pathogenic prevalence group of <i>n</i>, as described below;}}{p_n is the human pathogenic prevalence group of \eqn{n}, as described below;}
+#' * \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
 #' 
-#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*,\\*Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*,*Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
+#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*, *Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*, *Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
 #' 
 #' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first. 
 #' @export
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @examples 
 #' as.mo("E. coli")
 #' mo_uncertainties()
@@ -53,6 +57,9 @@
 #' mo_matching_score(x = "E. coli",
 #'                   n = c("Escherichia coli", "Entamoeba coli"))
 mo_matching_score <- function(x, n) {
+  meet_criteria(x, allow_class = c("character", "data.frame", "list"))
+  meet_criteria(n, allow_class = "character")
+  
  x <- parse_and_convert(x)
  # no dots and other non-whitespace characters
  x <- gsub("[^a-zA-Z0-9 \\(\\)]+", "", x)
--- a/R/mo_property.R
+++ b/R/mo_property.R
@@ -1,15 +1,15 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
-# Diagnostics & Advice, and University Medical Center Groningen.       # 
+# Diagnostics & Advice, and University Medical Center Groningen.       #
 #                                                                      #
 # This R package is free software; you can freely use and distribute   #
 # it for both personal and commercial purposes under the terms of the  #
@@ -20,33 +20,40 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Get properties of a microorganism
+#' Get Properties of a Microorganism
 #'
-#' Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with [as.mo()], which makes it possible to use microbial abbreviations, codes and names as input. Please see *Examples*.
-#' @inheritSection lifecycle Stable lifecycle
-#' @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 be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Use `language = NULL` or `language = ""` to prevent translation.
-#' @param ... other parameters passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'
-#' @param open browse the URL using [utils::browseURL()]
+#' Use these functions to return a specific property of a microorganism based on the latest accepted taxonomy. All input values will be evaluated internally with [as.mo()], which makes it possible to use microbial abbreviations, codes and names as input. See *Examples*.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x any [character] (vector) that can be coerced to a valid microorganism code with [as.mo()]. Can be left blank for auto-guessing the column containing microorganism codes if used in a data set, see *Examples*.
+#' @param property one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, quotes = TRUE)`, or must be `"shortname"`
+#' @param language language of the returned text, defaults to system language (see [get_locale()]) and can be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Also used to translate text like "no growth". Use `language = NULL` or `language = ""` to prevent translation.
+#' @param ... other arguments passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'
+#' @param ab any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
+#' @param open browse the URL using [`browseURL()`][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()]. Please refer to this example, knowing that *Escherichia blattae* was renamed to *Shimwellia blattae* in 2010:
 #' - `mo_name("Escherichia blattae")` will return `"Shimwellia blattae"` (with a message about the renaming)
 #' - `mo_ref("Escherichia blattae")` will return `"Burgess et al., 1973"` (with a message about the renaming)
 #' - `mo_ref("Shimwellia blattae")` will return `"Priest et al., 2010"` (without a message)
 #'
-#' The short name - [mo_shortname()] - almost always returns the first character of the genus and the full species, like `"E. coli"`. Exceptions are abbreviations of staphylococci (like *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (like *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will be considered *Escherichia coli*. In other words, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
-#' 
+#' The short name - [mo_shortname()] - almost always returns the first character of the genus and the full species, like `"E. coli"`. Exceptions are abbreviations of staphylococci (such as *"CoNS"*, Coagulase-Negative Staphylococci) and beta-haemolytic streptococci (such as *"GBS"*, Group B Streptococci). Please bear in mind that e.g. *E. coli* could mean *Escherichia coli* (kingdom of Bacteria) as well as *Entamoeba coli* (kingdom of Protozoa). Returning to the full name will be done using [as.mo()] internally, giving priority to bacteria and human pathogens, i.e. `"E. coli"` will be considered *Escherichia coli*. In other words, `mo_fullname(mo_shortname("Entamoeba coli"))` returns `"Escherichia coli"`.
+#'
 #' Since the top-level of the taxonomy is sometimes referred to as 'kingdom' and sometimes as 'domain', the functions [mo_kingdom()] and [mo_domain()] return the exact same results.
 #'
-#' The Gram stain - [mo_gramstain()] - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318)), 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`.
+#' The Gram stain - [mo_gramstain()] - will be determined based on the taxonomic kingdom and phylum. According to Cavalier-Smith (2002, [PMID 11837318](https://pubmed.ncbi.nlm.nih.gov/11837318)), 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`. Functions [mo_is_gram_negative()] and [mo_is_gram_positive()] always return `TRUE` or `FALSE` (except when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria.
+#' 
+#' Determination of yeasts - [mo_is_yeast()] - will be based on the taxonomic kingdom and class. *Budding yeasts* are fungi of the phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). *True yeasts* are aggregated into the underlying order Saccharomycetales. Thus, for all microorganisms that are fungi and member of the taxonomic class Saccharomycetes, the function will return `TRUE`. It returns `FALSE` otherwise (except when the input is `NA` or the MO code is `UNKNOWN`).
+#' 
+#' Intrinsic resistance - [mo_is_intrinsic_resistant()] - will be determined based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(3.2)`. The [mo_is_intrinsic_resistant()] functions can be vectorised over arguments `x` (input for microorganisms) and over `ab` (input for antibiotics).
 #'
-#' All output will be [translate]d where possible.
+#' All output [will be translated][translate] where possible.
 #'
 #' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
-#' @inheritSection mo_matching_score Matching score for microorganisms
+#' 
+#' SNOMED codes - [mo_snomed()] - are from the `r SNOMED_VERSION$current_source`. See the [microorganisms] data set for more info.
+#' @inheritSection mo_matching_score Matching Score for Microorganisms
 #' @inheritSection catalogue_of_life Catalogue of Life
 #' @inheritSection as.mo Source
 #' @rdname mo_property
@@ -55,12 +62,12 @@
 #' - An [integer] in case of [mo_year()]
 #' - A [list] in case of [mo_taxonomy()] and [mo_info()]
 #' - A named [character] in case of [mo_url()]
-#' - A [double] in case of [mo_snomed()]
+#' - A [numeric] in case of [mo_snomed()]
 #' - A [character] in all other cases
 #' @export
 #' @seealso [microorganisms]
-#' @inheritSection AMR Reference data publicly available
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Reference Data Publicly Available
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' # taxonomic tree -----------------------------------------------------------
 #' mo_kingdom("E. coli")         # "Bacteria"
@@ -122,7 +129,7 @@
 #' mo_shortname("S. pyo", Lancefield = TRUE) # "GAS" (='Group A Streptococci')
 #'
 #'
-#' # language support for German, Dutch, Spanish, Portuguese, Italian and French
+#' # language support  --------------------------------------------------------
 #' mo_gramstain("E. coli", language = "de")  # "Gramnegativ"
 #' mo_gramstain("E. coli", language = "nl")  # "Gram-negatief"
 #' mo_gramstain("E. coli", language = "es")  # "Gram negativo"
@@ -140,13 +147,40 @@
 #'             language = "nl")              # "Streptococcus groep A"
 #'
 #'
+#' # other --------------------------------------------------------------------
+#' 
+#' mo_is_yeast(c("Candida", "E. coli"))      # TRUE, FALSE
+#' 
+#' # gram stains and intrinsic resistance can also be used as a filter in dplyr verbs
+#' \donttest{
+#' if (require("dplyr")) {
+#'   example_isolates %>%
+#'     filter(mo_is_gram_positive())
+#'     
+#'   example_isolates %>%
+#'     filter(mo_is_intrinsic_resistant(ab = "vanco"))
+#' }
+#' 
+#' 
 #' # get a list with the complete taxonomy (from kingdom to subspecies)
 #' mo_taxonomy("E. coli")
-#' # get a list with the taxonomy, the authors, Gram-stain and URL to the online database
+#' # get a list with the taxonomy, the authors, Gram-stain,
+#' #   SNOMED codes, and URL to the online database
 #' mo_info("E. coli")
 #' }
+#' }
 mo_name <- function(x, language = get_locale(), ...) {
-  translate_AMR(mo_validate(x = x, property = "fullname", language = language, ...), language = language, only_unknown = FALSE)
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_name")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  translate_AMR(mo_validate(x = x, property = "fullname", language = language, ...),
+                language = language,
+                only_unknown = FALSE,
+                only_affect_mo_names = TRUE)
 }

 #' @rdname mo_property
@@ -156,6 +190,13 @@ mo_fullname <- mo_name
 #' @rdname mo_property
 #' @export
 mo_shortname <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_shortname")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x.mo <- as.mo(x, language = language, ...)
  
  metadata <- get_mo_failures_uncertainties_renamed()
@@ -171,63 +212,120 @@ mo_shortname <- function(x, language = get_locale(), ...) {
  
  # exceptions for where no species is known
  shortnames[shortnames %like% ".[.] spp[.]"] <- genera[shortnames %like% ".[.] spp[.]"]
-  # exceptions for Staphylococci
+  # exceptions for staphylococci
  shortnames[shortnames == "S. coagulase-negative"] <- "CoNS"
  shortnames[shortnames == "S. coagulase-positive"] <- "CoPS"
-  # exceptions for Streptococci: Streptococcus Group A -> GAS
+  # exceptions for streptococci: Group A Streptococcus -> GAS
  shortnames[shortnames %like% "S. group [ABCDFGHK]"] <- paste0("G", gsub("S. group ([ABCDFGHK])", "\\1", shortnames[shortnames %like% "S. group [ABCDFGHK]"]), "S")
  # unknown species etc.
  shortnames[shortnames %like% "unknown"] <- paste0("(", trimws(gsub("[^a-zA-Z -]", "", shortnames[shortnames %like% "unknown"])), ")")
  
+  shortnames[is.na(x.mo)] <- NA_character_
  load_mo_failures_uncertainties_renamed(metadata)
-  translate_AMR(shortnames, language = language, only_unknown = FALSE)
+  translate_AMR(shortnames, language = language, only_unknown = FALSE, only_affect_mo_names = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_subspecies <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_subspecies")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "subspecies", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_species <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_species")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "species", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_genus <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_genus")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "genus", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_family <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_family")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "family", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_order <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_order")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "order", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_class <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_class")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "class", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_phylum <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_phylum")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "phylum", language = language, ...), language = language, only_unknown = TRUE)
 }

 #' @rdname mo_property
 #' @export
 mo_kingdom <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_kingdom")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  translate_AMR(mo_validate(x = x, property = "kingdom", language = language, ...), language = language, only_unknown = TRUE)
 }

@@ -238,12 +336,29 @@ mo_domain <- mo_kingdom
 #' @rdname mo_property
 #' @export
 mo_type <- function(x, language = get_locale(), ...) {
-  translate_AMR(mo_validate(x = x, property = "kingdom", language = language, ...), language = language, only_unknown = FALSE)
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_type")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  x.mo <- as.mo(x, language = language, ...)
+  out <- mo_kingdom(x.mo, language = NULL)
+  out[which(mo_is_yeast(x.mo))] <- "Yeasts"
+  translate_AMR(out, language = language, only_unknown = FALSE)
 }

 #' @rdname mo_property
 #' @export
 mo_gramstain <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_gramstain")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x.mo <- as.mo(x, language = language, ...)
  metadata <- get_mo_failures_uncertainties_renamed()
  
@@ -272,21 +387,141 @@ mo_gramstain <- function(x, language = get_locale(), ...) {
  translate_AMR(x, language = language, only_unknown = FALSE)
 }

+#' @rdname mo_property
+#' @export
+mo_is_gram_negative <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_is_gram_negative")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  x.mo <- as.mo(x, language = language, ...)
+  metadata <- get_mo_failures_uncertainties_renamed()
+  grams <- mo_gramstain(x.mo, language = NULL)
+  load_mo_failures_uncertainties_renamed(metadata)
+  out <- grams == "Gram-negative" & !is.na(grams)
+  out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
+  out
+}
+
+#' @rdname mo_property
+#' @export
+mo_is_gram_positive <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_is_gram_positive")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  x.mo <- as.mo(x, language = language, ...)
+  metadata <- get_mo_failures_uncertainties_renamed()
+  grams <- mo_gramstain(x.mo, language = NULL)
+  load_mo_failures_uncertainties_renamed(metadata)
+  out <- grams == "Gram-positive" & !is.na(grams)
+  out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
+  out
+}
+
+#' @rdname mo_property
+#' @export
+mo_is_yeast <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_is_yeast")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  x.mo <- as.mo(x, language = language, ...)
+  metadata <- get_mo_failures_uncertainties_renamed()
+  
+  x.kingdom <- mo_kingdom(x.mo, language = NULL)
+  x.phylum <- mo_phylum(x.mo, language = NULL)
+  x.class <- mo_class(x.mo, language = NULL)
+  x.order <- mo_order(x.mo, language = NULL)
+  
+  load_mo_failures_uncertainties_renamed(metadata)
+  
+  out <- rep(FALSE, length(x))
+  out[x.kingdom == "Fungi" & x.class == "Saccharomycetes"] <- TRUE
+  out[x.mo %in% c(NA_character_, "UNKNOWN")] <- NA
+  out
+}
+
+#' @rdname mo_property
+#' @export
+mo_is_intrinsic_resistant <- function(x, ab, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_is_intrinsic_resistant")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(ab, allow_NA = FALSE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
+  x <- as.mo(x, language = language, ...)
+  ab <- as.ab(ab, language = NULL, flag_multiple_results = FALSE, info = FALSE)
+  
+  if (length(x) == 1 & length(ab) > 1) {
+    x <- rep(x, length(ab))
+  } else if (length(ab) == 1 & length(x) > 1) {
+    ab <- rep(ab, length(x))
+  }
+  if (length(x) != length(ab)) {
+    stop_("length of `x` and `ab` must be equal, or one of them must be of length 1.")
+  }
+  
+  # show used version number once per session (pkg_env will reload every session)
+  if (message_not_thrown_before("intrinsic_resistant_version", entire_session = TRUE)) {
+    message_("Determining intrinsic resistance based on ",
+             format_eucast_version_nr(3.2, markdown = FALSE), ". ",
+             font_red("This note will be shown once per session."))
+    remember_thrown_message("intrinsic_resistant_version", entire_session = TRUE)
+  }
+  
+  # runs against internal vector: INTRINSIC_R (see zzz.R)
+  paste(x, ab) %in% INTRINSIC_R
+}
+
 #' @rdname mo_property
 #' @export
 mo_snomed <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_snomed")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  mo_validate(x = x, property = "snomed", language = language, ...)
 }

 #' @rdname mo_property
 #' @export
 mo_ref <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_ref")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  mo_validate(x = x, property = "ref", language = language, ...)
 }

 #' @rdname mo_property
 #' @export
 mo_authors <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_authors")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- mo_validate(x = x, property = "ref", language = language, ...)
  # remove last 4 digits and presumably the comma and space that preceed them
  x[!is.na(x)] <- gsub(",? ?[0-9]{4}", "", x[!is.na(x)])
@@ -296,6 +531,13 @@ mo_authors <- function(x, language = get_locale(), ...) {
 #' @rdname mo_property
 #' @export
 mo_year <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_year")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- mo_validate(x = x, property = "ref", language = language, ...)
  # get last 4 digits
  x[!is.na(x)] <- gsub(".*([0-9]{4})$", "\\1", x[!is.na(x)])
@@ -305,23 +547,37 @@ mo_year <- function(x, language = get_locale(), ...) {
 #' @rdname mo_property
 #' @export
 mo_rank <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_rank")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  mo_validate(x = x, property = "rank", language = language, ...)
 }

 #' @rdname mo_property
 #' @export
 mo_taxonomy <- function(x, language = get_locale(),  ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_taxonomy")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- as.mo(x, language = language, ...)
  metadata <- get_mo_failures_uncertainties_renamed()
  
  result <- list(kingdom = mo_kingdom(x, language = language),
-                       phylum = mo_phylum(x, language = language),
-                       class = mo_class(x, language = language),
-                       order = mo_order(x, language = language),
-                       family = mo_family(x, language = language),
-                       genus = mo_genus(x, language = language),
-                       species = mo_species(x, language = language),
-                       subspecies = mo_subspecies(x, language = language))
+                 phylum = mo_phylum(x, language = language),
+                 class = mo_class(x, language = language),
+                 order = mo_order(x, language = language),
+                 family = mo_family(x, language = language),
+                 genus = mo_genus(x, language = language),
+                 species = mo_species(x, language = language),
+                 subspecies = mo_subspecies(x, language = language))
  
  load_mo_failures_uncertainties_renamed(metadata)
  result
@@ -330,6 +586,13 @@ mo_taxonomy <- function(x, language = get_locale(),  ...) {
 #' @rdname mo_property
 #' @export
 mo_synonyms <- function(x, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_synonyms")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- as.mo(x, language = language, ...)
  metadata <- get_mo_failures_uncertainties_renamed()
  
@@ -356,6 +619,13 @@ mo_synonyms <- function(x, language = get_locale(), ...) {
 #' @rdname mo_property
 #' @export
 mo_info <- function(x, language = get_locale(),  ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_info")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  x <- as.mo(x, language = language, ...)
  metadata <- get_mo_failures_uncertainties_renamed()
  
@@ -364,7 +634,8 @@ mo_info <- function(x, language = get_locale(),  ...) {
      list(synonyms = mo_synonyms(y),
           gramstain = mo_gramstain(y, language = language),
           url = unname(mo_url(y, open = FALSE)),
-           ref = mo_ref(y))))
+           ref = mo_ref(y),
+           snomed = unlist(mo_snomed(y)))))
  if (length(info) > 1) {
    names(info) <- mo_name(x)
    result <- info
@@ -379,23 +650,41 @@ mo_info <- function(x, language = get_locale(),  ...) {
 #' @rdname mo_property
 #' @export
 mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_url")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(open, allow_class = "logical", has_length = 1)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  
  mo <- as.mo(x = x, language = language, ... = ...)
  mo_names <- mo_name(mo)
  metadata <- get_mo_failures_uncertainties_renamed()
  
  df <- data.frame(mo, stringsAsFactors = FALSE) %pm>%
-   pm_left_join(pm_select(microorganisms, mo, source, species_id), by = "mo")
+    pm_left_join(pm_select(microorganisms, mo, source, species_id), by = "mo")
  df$url <- ifelse(df$source == "CoL",
-                   paste0(catalogue_of_life$url_CoL, "details/species/id/", df$species_id, "/"),
-                   ifelse(df$source == "DSMZ",
-                          paste0(catalogue_of_life$url_DSMZ, "/advanced_search?adv[taxon-name]=", gsub(" ", "+", mo_names), "/"),
-                          NA_character_))
+                   paste0(CATALOGUE_OF_LIFE$url_CoL, "details/species/id/", df$species_id, "/"),
+                   NA_character_)
  u <- df$url
+  u[mo_kingdom(mo) == "Bacteria"] <- paste0(CATALOGUE_OF_LIFE$url_LPSN, "/species/", gsub(" ", "-", tolower(mo_names), fixed = TRUE))
+  u[mo_kingdom(mo) == "Bacteria" & mo_rank(mo) == "genus"] <- gsub("/species/",
+                                                                   "/genus/",
+                                                                   u[mo_kingdom(mo) == "Bacteria" & mo_rank(mo) == "genus"],
+                                                                   fixed = TRUE)
+  u[mo_kingdom(mo) == "Bacteria" &
+      mo_rank(mo) %in% c("subsp.", "infraspecies")] <- gsub("/species/",
+                                                            "/subspecies/",
+                                                            u[mo_kingdom(mo) == "Bacteria" &
+                                                                mo_rank(mo) %in% c("subsp.", "infraspecies")],
+                                                            fixed = TRUE)
+  
  names(u) <- mo_names
  
  if (open == TRUE) {
    if (length(u) > 1) {
-      warning("only the first URL will be opened, as `browseURL()` only suports one string.")
+      warning_("Only the first URL will be opened, as `browseURL()` only suports one string.")
    }
    utils::browseURL(u[1L])
  }
@@ -408,54 +697,68 @@ mo_url <- function(x, open = FALSE, language = get_locale(), ...) {
 #' @rdname mo_property
 #' @export
 mo_property <- function(x, property = "fullname", language = get_locale(), ...) {
-  stop_ifnot(length(property) == 1L, "'property' must be of length 1")
-  stop_ifnot(property %in% colnames(microorganisms),
-             "invalid property: '", property, "' - use a column name of the `microorganisms` data set")
+  if (missing(x)) {
+    # this tries to find the data and an <mo> column
+    x <- find_mo_col(fn = "mo_property")
+  }
+  meet_criteria(x, allow_NA = TRUE)
+  meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(microorganisms))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
  
  translate_AMR(mo_validate(x = x, property = property, language = language, ...), language = language, only_unknown = TRUE)
 }

 mo_validate <- function(x, property, language, ...) {
-  
  check_dataset_integrity()
+  dots <- list(...)
+  Becker <- dots$Becker
+  if (is.null(Becker) | property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
+    Becker <- FALSE
+  }
+  Lancefield <- dots$Lancefield
+  if (is.null(Lancefield) | property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
+    Lancefield <- FALSE
+  }
+  has_Becker_or_Lancefield <- Becker %in% c(TRUE, "all") | Lancefield %in% c(TRUE, "all")
  
-  if (tryCatch(all(x[!is.na(x)] %in% MO_lookup$mo) & length(list(...)) == 0, error = function(e) FALSE)) {
+  if (tryCatch(all(x[!is.na(x)] %in% MO_lookup$mo) & !has_Becker_or_Lancefield, error = function(e) FALSE)) {
    # special case for mo_* functions where class is already <mo>
    return(MO_lookup[match(x, MO_lookup$mo), property, drop = TRUE])
  }
  
-  dots <- list(...)
-  Becker <- dots$Becker
-  if (is.null(Becker)) {
-    Becker <- FALSE
-  }
-  Lancefield <- dots$Lancefield
-  if (is.null(Lancefield)) {
-    Lancefield <- FALSE
-  }
-  
-  # try to catch an error when inputting an invalid parameter
+  # try to catch an error when inputting an invalid argument
  # so the 'call.' can be set to FALSE
  tryCatch(x[1L] %in% MO_lookup[1, property, drop = TRUE],
           error = function(e) stop(e$message, call. = FALSE))
-  
-  if (is.mo(x) 
-      & !Becker %in% c(TRUE, "all") 
-      & !Lancefield %in% c(TRUE, "all")) {
-    # this will not reset mo_uncertainties and mo_failures
-    # because it's already a valid MO
-    x <- exec_as.mo(x, property = property, initial_search = FALSE, language = language, ...)
-  } else if (!all(x %in% MO_lookup[, property, drop = TRUE])
-             | Becker %in% c(TRUE, "all")
-             | Lancefield %in% c(TRUE, "all")) {
+
+  if (!all(x[!is.na(x)] %in% MO_lookup[, property, drop = TRUE]) | has_Becker_or_Lancefield) {
    x <- exec_as.mo(x, property = property, language = language, ...)
  }
  
  if (property == "mo") {
-    return(to_class_mo(x))
+    return(set_clean_class(x, new_class = c("mo", "character")))
  } else if (property == "snomed") {
    return(as.double(eval(parse(text = x))))
  } else {
    return(x)
  }
 }
+
+find_mo_col <- function(fn) {
+  # this function tries to find an mo column in the data the function was called in,
+  # which is useful when functions are used within dplyr verbs
+  df <- get_current_data(arg_name = "x", call = -3) # will return an error if not found
+  mo <- NULL
+  try({
+    mo <- suppressMessages(search_type_in_df(df, "mo"))
+  }, silent = TRUE)
+  if (!is.null(df) && !is.null(mo) && is.data.frame(df)) {
+    if (message_not_thrown_before(fn = fn)) {
+      message_("Using column '", font_bold(mo), "' as input for ", fn, "()")
+      remember_thrown_message(fn = fn)
+    }
+    return(df[, mo, drop = TRUE])
+  } else {
+    stop_("argument `x` is missing and no column with info about microorganisms could be found.", call = -2)
+  }
+}
--- a/R/mo_source.R
+++ b/R/mo_source.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,30 +20,31 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' User-defined reference data set for microorganisms
+#' User-Defined Reference Data Set for Microorganisms
 #'
-#' @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()].
+#' @description These functions can be used to predefine your own reference to be used in [as.mo()] and consequently all [`mo_*`][mo_property()] functions (such as [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.
-#' @inheritSection lifecycle Stable lifecycle
-#' @param path location of your reference file, see Details. Can be `""`, `NULL` or `FALSE` to delete the reference file.
+#' This is **the fastest way** to have your organisation (or analysis) specific codes picked up and translated by this package, since you don't have to bother about it again after setting it up once.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param path location of your reference file, see *Details*. Can be `""`, `NULL` or `FALSE` to delete the reference file.
+#' @param destination destination of the compressed data file, default to the user's home directory.
 #' @rdname mo_source
 #' @name mo_source
 #' @aliases set_mo_source get_mo_source
-#' @details The reference file can be a text file separated 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 will need to have the `readxl` package installed.
+#' @details The reference file can be a text file separated 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 will 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$mo`][microorganisms] and must have a reference column with your own defined values. If all tests pass, [set_mo_source()] will read the file into R and will ask to export it to `"~/.mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. 
+#' [set_mo_source()] will check the file for validity: it must be a [data.frame], must have a column named `"mo"` which contains values from [`microorganisms$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 will ask to export it to `"~/mo_source.rds"`. The CRAN policy disallows packages to write to the file system, although '*exceptions may be allowed in interactive sessions if the package obtains confirmation from the user*'. For this reason, this function only works in interactive sessions so that the user can **specifically confirm and allow** that this file will be created. The destination of this file can be set with the `destination` argument and defaults to the user's home directory. It can also be set as an \R option, using `options(AMR_mo_source = "my/location/file.rds")`.
 #' 
-#' The created compressed data file `"~/.mo_source.rds"` will 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 an R option with `options(mo_source = path)`. Its timestamp will be saved with `options(mo_source_datetime = ...)`. 
+#' The created compressed data file `"mo_source.rds"` will be used at default for MO determination (function [as.mo()] and consequently all `mo_*` functions like [mo_genus()] and [mo_gramstain()]). The location and timestamp of the original file will be saved as an attribute to the compressed data file. 
 #' 
-#' The function [get_mo_source()] will return the data set by reading `"~/.mo_source.rds"` with [readRDS()]. If the original file has changed (by checking the aforementioned options `mo_source` and `mo_source_datetime`), it will call [set_mo_source()] to update the data file automatically if used in an interactive session.
+#' The function [get_mo_source()] will return the data set by reading `"mo_source.rds"` with [readRDS()]. If the original file has changed (by checking the location and timestamp of the original file), it will call [set_mo_source()] to update the data file automatically if used in an interactive session.
 #'
 #' Reading an Excel file (`.xlsx`) with only one row has a size of 8-9 kB. The compressed file created with [set_mo_source()] will then have a size of 0.1 kB and can be read by [get_mo_source()] in only a couple of microseconds (millionths of a second).
 #' 
-#' @section How to setup:
+#' @section How to Setup:
 #' 
 #' 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:
 #' 
@@ -60,16 +61,18 @@
 #' 
 #' ```
 #' set_mo_source("home/me/ourcodes.xlsx")
-#' #> NOTE: Created mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
-#' #>       (columns "Organisation XYZ" and "mo")
+#' #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from
+#' #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns 
+#' #>       "Organisation XYZ" and "mo"
 #' ```
 #'
-#' It has now created a file `"~/.mo_source.rds"` with the contents of our Excel file. Only the first column with foreign values and the 'mo' column will be kept when creating the RDS file.
+#' It has now created a file `"~/mo_source.rds"` with the contents of our Excel file. Only the first column with foreign values and the 'mo' column will be kept when creating the RDS file.
 #'
 #' And now we can use it in our functions:
 #' 
 #' ```
 #' as.mo("lab_mo_ecoli")
+#' #> Class <mo>
 #' #> [1] B_ESCHR_COLI
 #'
 #' mo_genus("lab_mo_kpneumoniae")
@@ -77,6 +80,9 @@
 #'
 #' # other input values still work too
 #' as.mo(c("Escherichia coli", "E. coli", "lab_mo_ecoli"))
+#' #> NOTE: Translation to one microorganism was guessed with uncertainty.
+#' #>       Use mo_uncertainties() to review it.
+#' #> Class <mo>
 #' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI
 #' ```
 #'
@@ -96,8 +102,10 @@
 #' 
 #' ```
 #' as.mo("lab_mo_ecoli")
-#' #> NOTE: Updated mo_source file '~/.mo_source.rds' from 'home/me/ourcodes.xlsx'
-#' #>       (columns "Organisation XYZ" and "mo")
+#' #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from 
+#' #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns
+#' #>        "Organisation XYZ" and "mo"
+#' #> Class <mo>
 #' #> [1] B_ESCHR_COLI
 #'
 #' mo_genus("lab_Staph_aureus")
@@ -108,39 +116,41 @@
 #' 
 #' ```
 #' set_mo_source(NULL)
-#' # Removed mo_source file '~/.mo_source.rds'.
+#' #> Removed mo_source file '/Users/me/mo_source.rds'
 #' ```
 #' 
-#' If the original Excel file is moved or deleted, the mo_source file will be removed upon the next use of [as.mo()]. If the mo_source file is manually deleted (i.e. without using [set_mo_source()]), the references to the mo_source file will be removed upon the next use of [as.mo()].
+#' If the original file (in the previous case an Excel file) is moved or deleted, the `mo_source.rds` file will be removed upon the next use of [as.mo()] or any [`mo_*`][mo_property()] function.
 #' @export
-#' @inheritSection AMR Read more on our website!
-set_mo_source <- function(path) {
+#' @inheritSection AMR Read more on Our Website!
+set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_source.rds")) {
+  meet_criteria(path, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(destination, allow_class = "character", has_length = 1)
+  stop_ifnot(destination %like% "[.]rds$", "the `destination` must be a file location with file extension .rds.")
  
-  file_location <- path.expand("~/mo_source.rds")
-  
-  stop_ifnot(interactive(), "This function can only be used in interactive mode, since it must ask for the user's permission to write a file to their home folder.")
-  stop_ifnot(length(path) == 1, "`path` must be of length 1")
+  mo_source_destination <- path.expand(destination)
  
+  stop_ifnot(interactive(), "this function can only be used in interactive mode, since it must ask for the user's permission to write a file to their home folder.")
+
  if (is.null(path) || path %in% c(FALSE, "")) {
-    options(mo_source = NULL)
-    options(mo_source_timestamp = NULL)
-    if (file.exists(file_location)) {
-      unlink(file_location)
-      message(font_red(paste0("Removed mo_source file '", font_bold(file_location), "'")))
+    pkg_env$mo_source <- NULL
+    if (file.exists(mo_source_destination)) {
+      unlink(mo_source_destination)
+      message_("Removed mo_source file '", font_bold(mo_source_destination), "'",
+               add_fn = font_red,
+               as_note = FALSE)
    }
    return(invisible())
  }
  
-  stop_ifnot(file.exists(path),
-             "file not found: ", path)
+  stop_ifnot(file.exists(path), "file not found: ", path)
  
  if (path %like% "[.]rds$") {
    df <- readRDS(path)
    
  } else if (path %like% "[.]xlsx?$") {
    # is Excel file (old or new)
-    read_excel <- import_fn("read_excel", "readxl")
-    df <- read_excel(path)
+    stop_ifnot_installed("readxl")
+    df <- readxl::read_excel(path)
    
  } else if (path %like% "[.]tsv$") {
    df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE)
@@ -150,13 +160,13 @@ set_mo_source <- function(path) {
    try(
      df <- utils::read.table(header = TRUE, sep = ",", stringsAsFactors = FALSE),
      silent = TRUE)
-    if (!mo_source_isvalid(df, stop_on_error = FALSE)) {
+    if (!check_validity_mo_source(df, stop_on_error = FALSE)) {
      # try tab
      try(
        df <- utils::read.table(header = TRUE, sep = "\t", stringsAsFactors = FALSE),
        silent = TRUE)
    }
-    if (!mo_source_isvalid(df, stop_on_error = FALSE)) {
+    if (!check_validity_mo_source(df, stop_on_error = FALSE)) {
      # try pipe
      try(
        df <- utils::read.table(header = TRUE, sep = "|", stringsAsFactors = FALSE),
@@ -165,7 +175,7 @@ set_mo_source <- function(path) {
  }
  
  # check integrity
-  mo_source_isvalid(df)
+  check_validity_mo_source(df)
  
  df <- subset(df, !is.na(mo))
  
@@ -177,18 +187,21 @@ set_mo_source <- function(path) {
  }
  
  df <- as.data.frame(df, stringAsFactors = FALSE)
+  df[, "mo"] <- set_clean_class(df[, "mo", drop = TRUE], c("mo", "character"))
  
  # success
-  if (file.exists(file_location)) {
+  if (file.exists(mo_source_destination)) {
    action <- "Updated"
  } else {
    action <- "Created"
    # only ask when file is created, not when it is updated
-    txt <- paste0("This will write create the new file '", 
-                  file_location, 
-                  "', for which your permission is needed.\n\nDo you agree that this file will be created? ")
-    if ("rsasdtudioapi" %in% rownames(utils::installed.packages())) {
-      showQuestion <- import_fn("showQuestion", "rstudioapi")
+    txt <- paste0(word_wrap(paste0("This will write create the new file '", 
+                                   mo_source_destination, 
+                                   "', for which your permission is needed.")),
+                  "\n\n",
+                  word_wrap("Do you agree that this file will be created?"))
+    showQuestion <- import_fn("showQuestion", "rstudioapi", error_on_fail = FALSE)
+    if (!is.null(showQuestion)) {
      q_continue <- showQuestion("Create new file in home directory", txt)
    } else {
      q_continue <- utils::menu(choices = c("OK", "Cancel"), graphics = FALSE, title = txt)
@@ -197,72 +210,67 @@ set_mo_source <- function(path) {
      return(invisible())
    }
  }
-  saveRDS(df, file_location)
-  options(mo_source = path)
-  options(mo_source_timestamp = as.character(file.info(path)$mtime))
-  message(font_blue(paste0("NOTE: ",
-                           action, " mo_source file '", font_bold(file_location), "'",
-                           " from '", font_bold(path), "'",
-                           '\n      (columns "', colnames(df)[1], '" and "', colnames(df)[2], '")')))
+  attr(df, "mo_source_location") <- path
+  attr(df, "mo_source_destination") <- mo_source_destination
+  attr(df, "mo_source_timestamp") <- file.mtime(path)
+  saveRDS(df, mo_source_destination)
+  pkg_env$mo_source <- df
+  message_(action, " mo_source file '", font_bold(mo_source_destination),
+           "' (", formatted_filesize(mo_source_destination),
+           ") from '", font_bold(path),
+           "' (", formatted_filesize(path),
+           '), columns "', colnames(df)[1], '" and "', colnames(df)[2], '"')
 }

 #' @rdname mo_source
 #' @export
-get_mo_source <- function() {
-  if (is.null(getOption("mo_source", NULL))) {
+get_mo_source <- function(destination = getOption("AMR_mo_source", "~/mo_source.rds")) {
+  if (!file.exists(path.expand(destination))) {
+    if (interactive()) {
+      # source file might have been deleted, so update reference
+      set_mo_source("")
+    }
    return(NULL)
  }
-  
-  if (!file.exists(path.expand("~/mo_source.rds"))) {
-    options(mo_source = NULL)
-    options(mo_source_timestamp = NULL)
-    message(font_blue("NOTE: Removed references to deleted mo_source file (see ?mo_source)"))
-    return(NULL)
+  if (is.null(pkg_env$mo_source)) {
+    pkg_env$mo_source <- readRDS(path.expand(destination))
  }
  
-  old_time <- as.POSIXct(getOption("mo_source_timestamp"))
-  new_time <- as.POSIXct(as.character(file.info(getOption("mo_source", ""))$mtime))
-  
-  if (is.na(new_time)) {
-    # source file was deleted, remove reference too
-    set_mo_source("")
-    return(NULL)
+  old_time <- attributes(pkg_env$mo_source)$mo_source_timestamp
+  new_time <- file.mtime(attributes(pkg_env$mo_source)$mo_source_location)
+  if (interactive() && !identical(old_time, new_time)) {
+    # source file was updated, also update reference
+    set_mo_source(attributes(pkg_env$mo_source)$mo_source_location)
  }
-  if (interactive() && new_time != old_time) {
-    # set updated source
-    set_mo_source(getOption("mo_source"))
-  }
-  file_location <- path.expand("~/mo_source.rds")
-  readRDS(file_location)
+  pkg_env$mo_source
 }

-mo_source_isvalid <- function(x, refer_to_name = "`reference_df`", stop_on_error = TRUE) {
-  
+check_validity_mo_source <- function(x, refer_to_name = "`reference_df`", stop_on_error = TRUE) {
  check_dataset_integrity()
  
-  if (deparse(substitute(x)) == "get_mo_source()") {
+  if (paste(deparse(substitute(x)), collapse = "") == "get_mo_source()") {
    return(TRUE)
  }
-  if (identical(x, get_mo_source())) {
+  if (is.null(pkg_env$mo_source) && (identical(x, get_mo_source()))) {
    return(TRUE)
  }
  if (is.null(x)) {
    if (stop_on_error == TRUE) {
-      stop(refer_to_name, " cannot be NULL", call. = FALSE)
+      stop_(refer_to_name, " cannot be NULL", call = FALSE)
    } else {
      return(FALSE)
    }
  }
  if (!is.data.frame(x)) {
    if (stop_on_error == TRUE) {
-      stop(refer_to_name, " must be a data.frame", call. = FALSE)
+      stop_(refer_to_name, " must be a data.frame", call = FALSE)
    } else {
      return(FALSE)
    }
  }
  if (!"mo" %in% colnames(x)) {
    if (stop_on_error == TRUE) {
-      stop(refer_to_name, " must contain a column 'mo'", call. = FALSE)
+      stop_(refer_to_name, " must contain a column 'mo'", call = FALSE)
    } else {
      return(FALSE)
    }
@@ -275,13 +283,27 @@ mo_source_isvalid <- function(x, refer_to_name = "`reference_df`", stop_on_error
      } else {
        plural <- ""
      }
-      stop("Value", plural, " ", paste0("'", invalid[, 1, drop = TRUE], "'", collapse = ", "), 
+      stop_("Value", plural, " ", vector_and(invalid[, 1, drop = TRUE], quotes = TRUE), 
           " found in ", tolower(refer_to_name), 
-           ", but with invalid microorganism code", plural, " ", paste0("'", invalid$mo, "'", collapse = ", "),
-           call. = FALSE)
+           ", but with invalid microorganism code", plural, " ", vector_and(invalid$mo, quotes = TRUE),
+           call = FALSE)
    } else {
      return(FALSE)
    }
  }
-  TRUE
+  if (colnames(x)[1] != "mo" & nrow(x) > length(unique(x[, 1, drop = TRUE]))) {
+    if (stop_on_error == TRUE) {
+      stop_(refer_to_name, " contains duplicate values in column '", colnames(x)[1], "'", call = FALSE)
+    } else {
+      return(FALSE)
+    }
+  }
+  if (colnames(x)[2] != "mo" & nrow(x) > length(unique(x[, 2, drop = TRUE]))) {
+    if (stop_on_error == TRUE) {
+      stop_(refer_to_name, " contains duplicate values in column '", colnames(x)[2], "'", call = FALSE)
+    } else {
+      return(FALSE)
+    }
+  }
+  return(TRUE)
 }
--- a/R/pca.R
+++ b/R/pca.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,33 +20,33 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' Principal Component Analysis (for AMR)
 #' 
 #' Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
-#' @inheritSection lifecycle Maturing lifecycle
-#' @param x a [data.frame] containing numeric columns
+#' @inheritSection lifecycle Stable Lifecycle
+#' @param x a [data.frame] containing [numeric] columns
 #' @param ... columns of `x` to be selected for PCA, can be unquoted since it supports quasiquotation.
 #' @inheritParams stats::prcomp
 #' @details The [pca()] function takes a [data.frame] as input and performs the actual PCA with the \R function [prcomp()].
 #' 
-#' The result of the [pca()] function is a [prcomp] object, with an additional attribute `non_numeric_cols` which is a vector with the column names of all columns that do not contain numeric values. These are probably the groups and labels, and will be used by [ggplot_pca()].
+#' The result of the [pca()] function is a [prcomp] object, with an additional attribute `non_numeric_cols` which is a vector with the column names of all columns that do not contain [numeric] values. These are probably the groups and labels, and will be used by [ggplot_pca()].
 #' @return An object of classes [pca] and [prcomp]
 #' @importFrom stats prcomp
 #' @export
+#' @inheritSection AMR Read more on Our Website!
 #' @examples 
-#' # `example_isolates` is a dataset available in the AMR package.
+#' # `example_isolates` is a data set available in the AMR package.
 #' # See ?example_isolates.
 #'
 #' \donttest{
-#' 
 #' if (require("dplyr")) {
 #'   # calculate the resistance per group first 
 #'   resistance_data <- example_isolates %>% 
 #'     group_by(order = mo_order(mo),       # group on anything, like order
-#'              genus = mo_genus(mo)) %>%   #  and genus as we do here
+#'              genus = mo_genus(mo)) %>%   #   and genus as we do here;
 #'     summarise_if(is.rsi, resistance)     # then get resistance of all drugs
 #'     
 #'   # now conduct PCA for certain antimicrobial agents
@@ -66,9 +66,12 @@ pca <- function(x,
                scale. = TRUE,
                tol = NULL,
                rank. = NULL) {
-  
-  stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
-  stop_if(any(dim(x) == 0), "`x` must contain rows and columns")
+  meet_criteria(x, allow_class = "data.frame")
+  meet_criteria(retx, allow_class = "logical", has_length = 1)
+  meet_criteria(center, allow_class = "logical", has_length = 1)
+  meet_criteria(scale., allow_class = "logical", has_length = 1)
+  meet_criteria(tol, allow_class = "numeric", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(rank., allow_class = "numeric", has_length = 1, allow_NULL = TRUE)
  
  # unset data.table, tibble, etc.
  # also removes groups made by dplyr::group_by
@@ -87,15 +90,15 @@ pca <- function(x,
          # this is to support quoted variables: df %pm>% pca("mycol1", "mycol2")
          new_list[[i]] <- x[, new_list[[i]]]
        } else {
-          # remove item - it's a parameter like `center`
+          # remove item - it's a argument like `center`
          new_list[[i]] <- NULL
        }
      }
    }
    
    x <- as.data.frame(new_list, stringsAsFactors = FALSE)
-    if (any(sapply(x, function(y) !is.numeric(y)))) {
-      warning("Be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with numeric variables only. Please see Examples in ?pca.")
+    if (any(vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y)))) {
+      warning_("Be sure to first calculate the resistance (or susceptibility) of variables with antimicrobial test results, since PCA works with [numeric] variables only. See Examples in ?pca.", call = FALSE)
    }
    
    # set column names
@@ -103,21 +106,59 @@ pca <- function(x,
             error = function(e) warning("column names could not be set"))
    
    # keep only numeric columns
-    x <- x[, sapply(x, function(y) is.numeric(y))]
+    x <- x[, vapply(FUN.VALUE = logical(1), x, function(y) is.numeric(y))]
    # bind the data set with the non-numeric columns
-    x <- cbind(x.bak[, sapply(x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
+    x <- cbind(x.bak[, vapply(FUN.VALUE = logical(1), x.bak, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE], x)
  }
  
- x <-  pm_ungroup(x)  # would otherwise select the grouping vars
+  x <- pm_ungroup(x)  # would otherwise select the grouping vars
  x <- x[rowSums(is.na(x)) == 0, ] # remove columns containing NAs
  
-  pca_data <- x[, which(sapply(x, function(x) is.numeric(x)))]
+  pca_data <- x[, which(vapply(FUN.VALUE = logical(1), x, function(x) is.numeric(x)))]
  
-  message(font_blue(paste0("NOTE: Columns selected for PCA: ", paste0(font_bold(colnames(pca_data)), collapse = "/"),
-                           ".\n      Total observations available: ", nrow(pca_data), ".")))
+  message_("Columns selected for PCA: ", vector_and(font_bold(colnames(pca_data), collapse = NULL), quotes = TRUE),
+           ". Total observations available: ", nrow(pca_data), ".")
  
-  pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol, rank. = rank.)
-  attr(pca_model, "non_numeric_cols") <- x[, sapply(x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
+  if (getRversion() < "3.4.0") {
+    # stats::prcomp prior to 3.4.0 does not have the 'rank.' argument
+    pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol)
+  } else {
+    pca_model <- prcomp(pca_data, retx = retx, center = center, scale. = scale., tol = tol, rank. = rank.)
+  }
+  groups <- x[, vapply(FUN.VALUE = logical(1), x, function(y) !is.numeric(y) & !all(is.na(y))), drop = FALSE]
+  rownames(groups) <- NULL
+  attr(pca_model, "non_numeric_cols") <- groups
  class(pca_model) <- c("pca", class(pca_model))
  pca_model
 }
+
+#' @method print pca
+#' @export
+#' @noRd
+print.pca <- function(x, ...) {
+  a <- attributes(x)$non_numeric_cols
+  if (!is.null(a)) {
+    print_pca_group(a)
+    class(x) <- class(x)[class(x) != "pca"]
+  }
+  print(x, ...)
+}
+
+#' @method summary pca
+#' @export
+#' @noRd
+summary.pca <- function(object, ...) {
+  a <- attributes(object)$non_numeric_cols
+  if (!is.null(a)) {
+    print_pca_group(a)
+    class(object) <- class(object)[class(object) != "pca"]
+  }
+  summary(object, ...)
+}
+
+print_pca_group <- function(a) {
+  grps <- sort(unique(a[, 1, drop = TRUE]))
+  cat("Groups (n=", length(grps), ", named as '", colnames(a)[1], "'):\n", sep = "")
+  print(grps)
+  cat("\n")
+}
--- a/R/plot.R
+++ b/R/plot.R
@@ -0,0 +1,733 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' Plotting for Classes `rsi`, `mic` and `disk`
+#' 
+#' Functions to plot classes `rsi`, `mic` and `disk`, with support for base R and `ggplot2`.
+#' @inheritSection lifecycle Stable Lifecycle
+#' @inheritSection AMR Read more on Our Website!
+#' @param x,data MIC values created with [as.mic()] or disk diffusion values created with [as.disk()]
+#' @param mapping aesthetic mappings to use for [`ggplot()`][ggplot2::ggplot()]
+#' @param main,title title of the plot
+#' @param xlab,ylab axis title
+#' @param mo any (vector of) text that can be coerced to a valid microorganism code with [as.mo()]
+#' @param ab any (vector of) text that can be coerced to a valid antimicrobial code with [as.ab()]
+#' @param guideline interpretation guideline to use, defaults to the latest included EUCAST guideline, see *Details*
+#' @param colours_RSI colours to use for filling in the bars, must be a vector of three values (in the order R, S and I). The default colours are colour-blind friendly.
+#' @param language language to be used to translate 'Susceptible', 'Increased exposure'/'Intermediate' and 'Resistant', defaults to system language (see [get_locale()]) and can be overwritten by setting the option `AMR_locale`, e.g. `options(AMR_locale = "de")`, see [translate]. Use `language = NULL` or `language = ""` to prevent translation.
+#' @param expand a [logical] to indicate whether the range on the x axis should be expanded between the lowest and highest value. For MIC values, intermediate values will be factors of 2 starting from the highest MIC value. For disk diameters, the whole diameter range will be filled.
+#' @details
+#' The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
+#' 
+#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` argument are: `r vector_and(AMR::rsi_translation$guideline, quotes = TRUE, reverse = TRUE)`.
+#' 
+#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
+#' @name plot
+#' @rdname plot
+#' @return The `ggplot` functions return a [`ggplot`][ggplot2::ggplot()] model that is extendible with any `ggplot2` function.
+#' @param ... arguments passed on to [as.rsi()]
+#' @examples 
+#' some_mic_values <- random_mic(size = 100)
+#' some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
+#' some_rsi_values <- random_rsi(50, prob_RSI = c(0.30, 0.55, 0.05))
+#' 
+#' plot(some_mic_values)
+#' plot(some_disk_values)
+#' plot(some_rsi_values)
+#' 
+#' # when providing the microorganism and antibiotic, colours will show interpretations:
+#' plot(some_mic_values, mo = "S. aureus", ab = "ampicillin")
+#' plot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
+#' 
+#' \donttest{
+#' if (require("ggplot2")) {
+#'   ggplot(some_mic_values)
+#'   ggplot(some_disk_values, mo = "Escherichia coli", ab = "cipro")
+#'   ggplot(some_rsi_values)
+#' }
+#' }
+NULL
+
+#' @method plot mic
+#' @importFrom graphics barplot axis mtext legend
+#' @export
+#' @rdname plot
+plot.mic <- function(x,
+                     main = paste("MIC values of", deparse(substitute(x))),
+                     ylab = "Frequency",
+                     xlab = "Minimum Inhibitory Concentration (mg/L)",
+                     mo = NULL,
+                     ab = NULL,
+                     guideline = "EUCAST",
+                     colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                     language = get_locale(),
+                     expand = TRUE,
+                     ...) {
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if (length(colours_RSI) == 1) {
+    colours_RSI <- rep(colours_RSI, 3)
+  }
+  main <- gsub(" +", " ", paste0(main, collapse = " "))
+  
+  x <- plot_prepare_table(x, expand = expand)
+  
+  cols_sub <- plot_colours_subtitle_guideline(x = x, 
+                                              mo = mo, 
+                                              ab = ab,
+                                              guideline = guideline,
+                                              colours_RSI = colours_RSI, 
+                                              fn = as.mic,
+                                              language = language,
+                                              ...)
+  barplot(x,
+          col = cols_sub$cols,
+          main = main,
+          ylim = c(0, max(x) * ifelse(any(colours_RSI %in% cols_sub$cols), 1.1, 1)),
+          ylab = ylab,
+          xlab = xlab,
+          axes = FALSE)
+  axis(2, seq(0, max(x)))
+  if (!is.null(cols_sub$sub)) {
+    mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
+  }
+  
+  if (any(colours_RSI %in% cols_sub$cols)) {
+    legend_txt <- character(0)
+    legend_col <- character(0)
+    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
+      legend_txt <- "Susceptible"
+      legend_col <- colours_RSI[2]
+    }
+    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
+      legend_col <- c(legend_col, colours_RSI[3])
+    }
+    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, "Resistant")
+      legend_col <- c(legend_col, colours_RSI[1])
+    }
+    legend("top",
+           x.intersp = 0.5,
+           legend = translate_AMR(legend_txt, language = language),
+           fill = legend_col,
+           horiz = TRUE,
+           cex = 0.75,
+           box.lwd = 0,
+           box.col = "#FFFFFF55",
+           bg = "#FFFFFF55")
+  }
+}
+
+#' @method barplot mic
+#' @export
+#' @noRd
+barplot.mic <- function(height,
+                        main = paste("MIC values of", deparse(substitute(height))),
+                        ylab = "Frequency",
+                        xlab = "Minimum Inhibitory Concentration (mg/L)",
+                        mo = NULL,
+                        ab = NULL,
+                        guideline = "EUCAST",
+                        colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                        language = get_locale(),
+                        expand = TRUE,
+                        ...) {
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  main <- gsub(" +", " ", paste0(main, collapse = " "))
+  
+  plot(x = height,
+       main = main,
+       ylab = ylab,
+       xlab = xlab,
+       mo = mo,
+       ab = ab,
+       guideline = guideline,
+       colours_RSI = colours_RSI,
+       ...)
+}
+
+#' @method ggplot mic
+#' @rdname plot
+# will be exported using s3_register() in R/zzz.R
+ggplot.mic <- function(data,
+                       mapping = NULL,
+                       title = paste("MIC values of", deparse(substitute(data))),
+                       ylab = "Frequency",
+                       xlab = "Minimum Inhibitory Concentration (mg/L)",
+                       mo = NULL,
+                       ab = NULL,
+                       guideline = "EUCAST",
+                       colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                       language = get_locale(),
+                       expand = TRUE,
+                       ...) {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if ("main" %in% names(list(...))) {
+    title <- list(...)$main
+  }
+  if (!is.null(title)) {
+    title <- gsub(" +", " ", paste0(title, collapse = " "))
+  }
+  
+  x <- plot_prepare_table(data, expand = expand)
+  cols_sub <- plot_colours_subtitle_guideline(x = x, 
+                                              mo = mo, 
+                                              ab = ab,
+                                              guideline = guideline,
+                                              colours_RSI = colours_RSI, 
+                                              fn = as.mic,
+                                              language = language,
+                                              ...)
+  df <- as.data.frame(x, stringsAsFactors = TRUE)
+  colnames(df) <- c("mic", "count")
+  df$cols <- cols_sub$cols
+  df$cols[df$cols == colours_RSI[1]] <- "Resistant"
+  df$cols[df$cols == colours_RSI[2]] <- "Susceptible"
+  df$cols[df$cols == colours_RSI[3]] <- plot_name_of_I(cols_sub$guideline)
+  df$cols <- factor(translate_AMR(df$cols, language = language),
+                    levels = translate_AMR(c("Susceptible", plot_name_of_I(cols_sub$guideline), "Resistant"),
+                                           language = language),
+                    ordered = TRUE)
+  if (!is.null(mapping)) {
+    p <- ggplot2::ggplot(df, mapping = mapping)
+  } else {
+    p <- ggplot2::ggplot(df)
+  }
+  
+  if (any(colours_RSI %in% cols_sub$cols)) {
+    vals <- c("Resistant" = colours_RSI[1],
+              "Susceptible" = colours_RSI[2],
+              "Incr. exposure" = colours_RSI[3],
+              "Intermediate" = colours_RSI[3])
+    names(vals) <- translate_AMR(names(vals), language = language)
+    p <- p +
+      ggplot2::geom_col(ggplot2::aes(x = mic, y = count, fill = cols)) + 
+      ggplot2::scale_fill_manual(values = vals,
+                                 name = NULL)
+  } else {
+    p <- p +
+      ggplot2::geom_col(ggplot2::aes(x = mic, y = count))
+  }
+  
+  p +
+    ggplot2::labs(title = title, x = xlab, y = ylab, subtitle = cols_sub$sub)
+}
+
+#' @method plot disk
+#' @export
+#' @importFrom graphics barplot axis mtext legend
+#' @rdname plot
+plot.disk <- function(x,
+                      main = paste("Disk zones of", deparse(substitute(x))),
+                      ylab = "Frequency",
+                      xlab = "Disk diffusion diameter (mm)",
+                      mo = NULL,
+                      ab = NULL,
+                      guideline = "EUCAST",
+                      colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                      language = get_locale(),
+                      expand = TRUE,
+                      ...) {
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if (length(colours_RSI) == 1) {
+    colours_RSI <- rep(colours_RSI, 3)
+  }
+  main <- gsub(" +", " ", paste0(main, collapse = " "))
+  
+  x <- plot_prepare_table(x, expand = expand)
+  
+  cols_sub <- plot_colours_subtitle_guideline(x = x, 
+                                              mo = mo, 
+                                              ab = ab,
+                                              guideline = guideline,
+                                              colours_RSI = colours_RSI, 
+                                              fn = as.disk,
+                                              language = language,
+                                              ...)
+  
+  barplot(x,
+          col = cols_sub$cols,
+          main = main,
+          ylim = c(0, max(x) * ifelse(any(colours_RSI %in% cols_sub$cols), 1.1, 1)),
+          ylab = ylab,
+          xlab = xlab,
+          axes = FALSE)
+  axis(2, seq(0, max(x)))
+  if (!is.null(cols_sub$sub)) {
+    mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
+  }
+  
+  if (any(colours_RSI %in% cols_sub$cols)) {
+    legend_txt <- character(0)
+    legend_col <- character(0)
+    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
+      legend_txt <- "Resistant"
+      legend_col <- colours_RSI[1]
+    }
+    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
+      legend_col <- c(legend_col, colours_RSI[3])
+    }
+    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, "Susceptible")
+      legend_col <- c(legend_col, colours_RSI[2])
+    }
+    legend("top", 
+           x.intersp = 0.5,
+           legend = translate_AMR(legend_txt, language = language),
+           fill = legend_col,
+           horiz = TRUE,
+           cex = 0.75,
+           box.lwd = 0,
+           box.col = "#FFFFFF55",
+           bg = "#FFFFFF55")
+  }
+}
+
+#' @method barplot disk
+#' @export
+#' @noRd
+barplot.disk <- function(height,
+                         main = paste("Disk zones of", deparse(substitute(height))),
+                         ylab = "Frequency",
+                         xlab = "Disk diffusion diameter (mm)",
+                         mo = NULL,
+                         ab = NULL,
+                         guideline = "EUCAST",
+                         colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                         language = get_locale(),
+                         expand = TRUE,
+                         ...) {
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  main <- gsub(" +", " ", paste0(main, collapse = " "))
+  
+  plot(x = height,
+       main = main,
+       ylab = ylab,
+       xlab = xlab,
+       mo = mo,
+       ab = ab,
+       guideline = guideline,
+       colours_RSI = colours_RSI,
+       ...)
+}
+
+#' @method ggplot disk
+#' @rdname plot
+# will be exported using s3_register() in R/zzz.R
+ggplot.disk <- function(data,
+                        mapping = NULL,
+                        title = paste("Disk zones of", deparse(substitute(data))),
+                        ylab = "Frequency",
+                        xlab = "Disk diffusion diameter (mm)",
+                        mo = NULL,
+                        ab = NULL,
+                        guideline = "EUCAST",
+                        colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                        language = get_locale(),
+                        expand = TRUE,
+                        ...) {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
+  meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
+  meet_criteria(guideline, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if ("main" %in% names(list(...))) {
+    title <- list(...)$main
+  }
+  if (!is.null(title)) {
+    title <- gsub(" +", " ", paste0(title, collapse = " "))
+  }
+  
+  x <- plot_prepare_table(data, expand = expand)
+  cols_sub <- plot_colours_subtitle_guideline(x = x,
+                                              mo = mo,
+                                              ab = ab,
+                                              guideline = guideline,
+                                              colours_RSI = colours_RSI,
+                                              fn = as.disk,
+                                              language = language,
+                                              ...)
+  df <- as.data.frame(x, stringsAsFactors = TRUE)
+  colnames(df) <- c("disk", "count")
+  df$cols <- cols_sub$cols
+  
+  df$cols[df$cols == colours_RSI[1]] <- "Resistant"
+  df$cols[df$cols == colours_RSI[2]] <- "Susceptible"
+  df$cols[df$cols == colours_RSI[3]] <- plot_name_of_I(cols_sub$guideline)
+  df$cols <- factor(translate_AMR(df$cols, language = language),
+                    levels = translate_AMR(c("Susceptible", plot_name_of_I(cols_sub$guideline), "Resistant"),
+                                           language = language),
+                    ordered = TRUE)
+  if (!is.null(mapping)) {
+    p <- ggplot2::ggplot(df, mapping = mapping)
+  } else {
+    p <- ggplot2::ggplot(df)
+  }
+  
+  if (any(colours_RSI %in% cols_sub$cols)) {
+    vals <- c("Resistant" = colours_RSI[1],
+              "Susceptible" = colours_RSI[2],
+              "Incr. exposure" = colours_RSI[3],
+              "Intermediate" = colours_RSI[3])
+    names(vals) <- translate_AMR(names(vals), language = language)
+    p <- p +
+      ggplot2::geom_col(ggplot2::aes(x = disk, y = count, fill = cols)) + 
+      ggplot2::scale_fill_manual(values = vals,
+                                 name = NULL)
+  } else {
+    p <- p +
+      ggplot2::geom_col(ggplot2::aes(x = disk, y = count))
+  }
+  
+  p +
+    ggplot2::labs(title = title, x = xlab, y = ylab, subtitle = cols_sub$sub)
+}
+
+#' @method plot rsi
+#' @export
+#' @importFrom graphics plot text axis
+#' @rdname plot
+plot.rsi <- function(x,
+                     ylab = "Percentage",
+                     xlab = "Antimicrobial Interpretation",
+                     main = paste("Resistance Overview of", deparse(substitute(x))),
+                     ...) {
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  
+  data <- as.data.frame(table(x), stringsAsFactors = FALSE)
+  colnames(data) <- c("x", "n")
+  data$s <- round((data$n / sum(data$n)) * 100, 1)
+  
+  if (!"S" %in% data$x) {
+    data <- rbind(data, data.frame(x = "S", n = 0, s = 0, stringsAsFactors = FALSE),
+                  stringsAsFactors = FALSE)
+  }
+  if (!"I" %in% data$x) {
+    data <- rbind(data, data.frame(x = "I", n = 0, s = 0, stringsAsFactors = FALSE),
+                  stringsAsFactors = FALSE)
+  }
+  if (!"R" %in% data$x) {
+    data <- rbind(data, data.frame(x = "R", n = 0, s = 0, stringsAsFactors = FALSE),
+                  stringsAsFactors = FALSE)
+  }
+  
+  data$x <- factor(data$x, levels = c("S", "I", "R"), ordered = TRUE)
+  
+  ymax <- pm_if_else(max(data$s) > 95, 105, 100)
+  
+  plot(x = data$x,
+       y = data$s,
+       lwd = 2,
+       ylim = c(0, ymax),
+       ylab = ylab,
+       xlab = xlab,
+       main = main,
+       axes = FALSE)
+  # x axis
+  axis(side = 1, at = 1:pm_n_distinct(data$x), labels = levels(data$x), lwd = 0)
+  # y axis, 0-100%
+  axis(side = 2, at = seq(0, 100, 5))
+  
+  text(x = data$x,
+       y = data$s + 4,
+       labels = paste0(data$s, "% (n = ", data$n, ")"))
+}
+
+
+#' @method barplot rsi
+#' @importFrom graphics barplot axis
+#' @export
+#' @noRd
+barplot.rsi <- function(height,
+                        main = paste("Resistance Overview of", deparse(substitute(height))),
+                        xlab = "Antimicrobial Interpretation",
+                        ylab = "Frequency",
+                        colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                        language = get_locale(),
+                        expand = TRUE,
+                        ...) {
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
+  meet_criteria(expand, allow_class = "logical", has_length = 1)
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if (length(colours_RSI) == 1) {
+    colours_RSI <- rep(colours_RSI, 3)
+  } else {
+    colours_RSI <- c(colours_RSI[2], colours_RSI[3], colours_RSI[1])
+  }
+  main <- gsub(" +", " ", paste0(main, collapse = " "))
+  
+  x <- table(height)
+  x <- x[c(1, 2, 3)]
+  barplot(x,
+          col = colours_RSI,
+          xlab = xlab,
+          main = main,
+          ylab = ylab,
+          axes = FALSE)
+  axis(2, seq(0, max(x)))
+}
+
+#' @method ggplot rsi
+#' @rdname plot
+# will be exported using s3_register() in R/zzz.R
+ggplot.rsi <- function(data,
+                       mapping = NULL,
+                       title = paste("Resistance Overview of", deparse(substitute(data))),
+                       xlab = "Antimicrobial Interpretation",
+                       ylab = "Frequency",
+                       colours_RSI = c("#ED553B", "#3CAEA3", "#F6D55C"),
+                       language = get_locale(),
+                       ...) {
+  stop_ifnot_installed("ggplot2")
+  meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
+  meet_criteria(ylab, allow_class = "character", has_length = 1)
+  meet_criteria(xlab, allow_class = "character", has_length = 1)
+  meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
+  
+  # translate if not specifically set
+  if (missing(ylab)) {
+    ylab <- translate_AMR(ylab, language = language)
+  }
+  if (missing(xlab)) {
+    xlab <- translate_AMR(xlab, language = language)
+  }
+  
+  if ("main" %in% names(list(...))) {
+    title <- list(...)$main
+  }
+  if (!is.null(title)) {
+    title <- gsub(" +", " ", paste0(title, collapse = " "))
+  }
+  
+  if (length(colours_RSI) == 1) {
+    colours_RSI <- rep(colours_RSI, 3)
+  }
+  
+  df <- as.data.frame(table(data), stringsAsFactors = TRUE)
+  colnames(df) <- c("rsi", "count")
+  if (!is.null(mapping)) {
+    p <- ggplot2::ggplot(df, mapping = mapping)
+  } else {
+    p <- ggplot2::ggplot(df)
+  }
+  
+  p +
+    ggplot2::geom_col(ggplot2::aes(x = rsi, y = count, fill = rsi)) + 
+    ggplot2::scale_fill_manual(values = c("R" = colours_RSI[1],
+                                          "S" = colours_RSI[2],
+                                          "I" = colours_RSI[3])) +
+    ggplot2::labs(title = title, x = xlab, y = ylab) +
+    ggplot2::theme(legend.position = "none")
+}
+
+plot_prepare_table <- function(x, expand) {
+  x <- x[!is.na(x)]
+  stop_if(length(x) == 0, "no observations to plot", call = FALSE)
+  if (is.mic(x)) {
+    if (expand == TRUE) {
+      # expand range for MIC by adding factors of 2 from lowest to highest so all MICs in between also print
+      extra_range <- max(x) / 2
+      while (min(extra_range) / 2 > min(x)) {
+        extra_range <- c(min(extra_range) / 2, extra_range)
+      }
+      nms <- extra_range
+      extra_range <- rep(0, length(extra_range))
+      names(extra_range) <- nms
+      x <- table(droplevels(x, as.mic = FALSE))
+      extra_range <- extra_range[!names(extra_range) %in% names(x)]
+      x <- as.table(c(x, extra_range))
+    } else {
+      x <- table(droplevels(x, as.mic = FALSE))
+    }
+    x <- x[order(as.double(as.mic(names(x))))]
+  } else if (is.disk(x)) {
+    if (expand == TRUE) {
+      # expand range for disks from lowest to highest so all mm's in between also print
+      extra_range <- rep(0, max(x) - min(x) - 1)
+      names(extra_range) <- seq(min(x) + 1, max(x) - 1)
+      x <- table(x)
+      extra_range <- extra_range[!names(extra_range) %in% names(x)]
+      x <- as.table(c(x, extra_range))
+    } else {
+      x <- table(x)
+    }
+    x <- x[order(as.double(names(x)))]
+  }
+  as.table(x)
+}
+
+plot_name_of_I <- function(guideline) {
+  if (guideline %unlike% "CLSI" && as.double(gsub("[^0-9]+", "", guideline)) >= 2019) {
+    # interpretation since 2019
+    "Incr. exposure"
+  } else {
+    # interpretation until 2019
+    "Intermediate"
+  }
+}
+
+plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_RSI, fn, language, ...) {
+  guideline <- get_guideline(guideline, AMR::rsi_translation)
+  if (!is.null(mo) && !is.null(ab)) {
+    # interpret and give colour based on MIC values
+    mo <- as.mo(mo)
+    ab <- as.ab(ab)
+    rsi <- suppressWarnings(suppressMessages(as.rsi(fn(names(x)), mo = mo, ab = ab, guideline = guideline, ...)))
+    cols <- character(length = length(rsi))
+    cols[is.na(rsi)] <- "#BEBEBE"
+    cols[rsi == "R"] <- colours_RSI[1]
+    cols[rsi == "S"] <- colours_RSI[2]
+    cols[rsi == "I"] <- colours_RSI[3]
+    moname <- mo_name(mo, language = language)
+    abname <- ab_name(ab, language = language)
+    if (all(cols == "#BEBEBE")) {
+      message_("No ", guideline, " interpretations found for ", 
+               ab_name(ab, language = NULL, tolower = TRUE), " in ", moname)
+      guideline_txt <- ""
+    } else {
+      guideline_txt <- paste0("(", guideline, ")")
+    }
+    sub <- bquote(.(abname)~"-"~italic(.(moname))~.(guideline_txt))
+  } else {
+    cols <- "#BEBEBE"
+    sub <- NULL
+  }
+  list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
+}
--- a/R/proportion.R
+++ b/R/proportion.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,34 +20,34 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Calculate microbial resistance
+#' 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 `summarise()` from the `dplyr` package and also support grouped variables, please see *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 `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
 #'
 #' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
-#' @inheritSection lifecycle Stable lifecycle
-#' @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
+#' @inheritSection lifecycle Stable Lifecycle
+#' @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()]. Use a value 
+#' @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 `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`.
+#' @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 argument `combine_IR`, but this now follows the redefinition by EUCAST about the interpretation 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 argument `combine_SI`.
 #' @inheritSection as.rsi Interpretation of R and S/I
 #' @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.
+#' **Remember that you should filter your data 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()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` parameter).*
+#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
 #'
 #' 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. It also supports grouped variables. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates.
-#' @section Combination therapy:
+#' @section 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, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
 #'
 #' ```
@@ -88,7 +88,7 @@
 #' @aliases portion
 #' @name proportion
 #' @export
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' # example_isolates is a data set available in the AMR package.
 #' ?example_isolates
@@ -103,6 +103,7 @@
 #' proportion_IR(example_isolates$AMX)
 #' proportion_R(example_isolates$AMX)
 #'
+#' \donttest{
 #' if (require("dplyr")) {
 #'   example_isolates %>%
 #'     group_by(hospital_id) %>%
@@ -161,6 +162,7 @@
 #'     group_by(hospital_id) %>%
 #'     proportion_df(translate = FALSE)
 #' }
+#' }
 resistance <- function(...,
                       minimum = 30,
                       as_percent = FALSE,
@@ -266,7 +268,6 @@ proportion_df <- function(data,
                          as_percent = FALSE,
                          combine_SI = TRUE,
                          combine_IR = FALSE) {
-  
  rsi_calc_df(type = "proportion",
              data = data,
              translate_ab = translate_ab,
--- a/R/random.R
+++ b/R/random.R
@@ -0,0 +1,140 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+#' Random MIC Values/Disk Zones/RSI Generation
+#'
+#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
+#' @inheritSection lifecycle Stable Lifecycle 
+#' @param size desired size of the returned vector
+#' @param mo any [character] that can be coerced to a valid microorganism code with [as.mo()]
+#' @param ab any [character] that can be coerced to a valid antimicrobial agent code with [as.ab()]
+#' @param prob_RSI a vector of length 3: the probabilities for R (1st value), S (2nd value) and I (3rd value)
+#' @param ... ignored, only in place to allow future extensions
+#' @details The base R function [sample()] is used for generating values.
+#' 
+#' Generated values are based on the latest EUCAST guideline implemented in the [rsi_translation] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument.
+#' @return class `<mic>` for [random_mic()] (see [as.mic()]) and class `<disk>` for [random_disk()] (see [as.disk()])
+#' @name random
+#' @rdname random
+#' @export
+#' @inheritSection AMR Read more on Our Website!
+#' @examples
+#' random_mic(100)
+#' random_disk(100)
+#' random_rsi(100)
+#' 
+#' \donttest{
+#' # make the random generation more realistic by setting a bug and/or drug:
+#' random_mic(100, "Klebsiella pneumoniae")                 # range 0.0625-64
+#' random_mic(100, "Klebsiella pneumoniae", "meropenem")    # range 0.0625-16
+#' random_mic(100, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
+#' 
+#' random_disk(100, "Klebsiella pneumoniae")                  # range 8-50
+#' random_disk(100, "Klebsiella pneumoniae", "ampicillin")    # range 11-17
+#' random_disk(100, "Streptococcus pneumoniae", "ampicillin") # range 12-27
+#' }
+random_mic <- function(size, mo = NULL, ab = NULL, ...) {
+  random_exec("MIC", size = size, mo = mo, ab = ab)
+}
+
+#' @rdname random
+#' @export
+random_disk <- function(size, mo = NULL, ab = NULL, ...) {
+  random_exec("DISK", size = size, mo = mo, ab = ab)
+}
+
+#' @rdname random
+#' @export
+random_rsi <- function(size, prob_RSI = c(0.33, 0.33, 0.33), ...) {
+  sample(as.rsi(c("R", "S", "I")), size = size, replace = TRUE, prob = prob_RSI)
+}
+
+random_exec <- function(type, size, mo = NULL, ab = NULL) {
+  df <- rsi_translation %pm>%
+    pm_filter(guideline %like% "EUCAST") %pm>%
+    pm_arrange(pm_desc(guideline)) %pm>%
+    subset(guideline == max(guideline) &
+              method == type)
+  
+  if (!is.null(mo)) {
+    mo_coerced <- as.mo(mo)
+    mo_include <- c(mo_coerced,
+                    as.mo(mo_genus(mo_coerced)),
+                    as.mo(mo_family(mo_coerced)),
+                    as.mo(mo_order(mo_coerced)))
+    df_new <- df %pm>%
+      subset(mo %in% mo_include)
+    if (nrow(df_new) > 0) {
+      df <- df_new
+    } else {
+      warning_("No rows found that match mo '", mo, "', ignoring argument `mo`", call = FALSE)
+    }
+  }
+  
+  if (!is.null(ab)) {
+    ab_coerced <- as.ab(ab)
+    df_new <- df %pm>%
+      subset(ab %in% ab_coerced)
+    if (nrow(df_new) > 0) {
+      df <- df_new
+    } else {
+      warning_("No rows found that match ab '", ab, "', ignoring argument `ab`", call = FALSE)
+    }
+  }
+  
+  if (type == "MIC") {
+    # all valid MIC levels
+    valid_range <- as.mic(levels(as.mic(1)))
+    set_range_max <- max(df$breakpoint_R)
+    if (log(set_range_max, 2) %% 1 == 0) {
+      # return powers of 2
+      valid_range <- unique(as.double(valid_range))
+      # add 1-3 higher MIC levels to set_range_max
+      set_range_max <- 2 ^ (log(set_range_max, 2) + sample(c(1:3), 1))
+      set_range <- as.mic(valid_range[log(valid_range, 2) %% 1 == 0 & valid_range <= set_range_max])
+    } else {
+      # no power of 2, return factors of 2 to left and right side
+      valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3))))
+      set_range <- valid_mics[!is.na(valid_mics)]
+    }
+    out <- as.mic(sample(set_range, size = size, replace = TRUE))
+    # 50% chance that lowest will get <= and highest will get >=
+    if (stats::runif(1) > 0.5) {
+      out[out == min(out)] <- paste0("<=", out[out == min(out)])
+    }
+    if (stats::runif(1) > 0.5) {
+      out[out == max(out)] <- paste0(">=", out[out == max(out)])
+    }
+    return(out)
+  } else if (type == "DISK") {
+    set_range <- seq(from = as.integer(min(df$breakpoint_R) / 1.25),
+                     to = as.integer(max(df$breakpoint_S) * 1.25),
+                     by = 1)
+    out <- sample(set_range, size = size, replace = TRUE)
+    out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)
+    out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE)
+    return(as.disk(out))
+  }
+}
--- a/R/resistance_predict.R
+++ b/R/resistance_predict.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,30 +20,30 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' Predict antimicrobial resistance
 #'
 #' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns `se_min` and `se_max`. See *Examples* for a real live example.
-#' @inheritSection lifecycle Maturing lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
 #' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
 #' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date`
 #' @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 `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 `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 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 interpretation 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
+#' @param ribbon a [logical] to indicate whether a ribbon should be shown (default) or error bars
+#' @param ... arguments passed on to functions
 #' @inheritSection as.rsi Interpretation of R and S/I
 #' @inheritParams first_isolate
 #' @inheritParams graphics::plot
-#' @details Valid options for the statistical model (parameter `model`) are:
+#' @details Valid options for the statistical model (argument `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
@@ -56,20 +56,21 @@
 #' - `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)$model`, please see *Examples*.
+#' Furthermore, the model itself is available as an attribute: `attributes(x)$model`, see *Examples*.
 #' @seealso The [proportion()] functions to calculate resistance
 #' 
 #' Models: [lm()] [glm()]
 #' @rdname resistance_predict
 #' @export
 #' @importFrom stats predict glm lm
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @examples
 #' x <- resistance_predict(example_isolates, 
 #'                         col_ab = "AMX",
 #'                         year_min = 2010,
 #'                         model = "binomial")
 #' plot(x)
+#' \donttest{
 #' if (require("ggplot2")) {
 #'   ggplot_rsi_predict(x)
 #' }
@@ -114,6 +115,7 @@
 #'          x = "Year") +
 #'     theme_minimal(base_size = 13)
 #' }
+#' }
 resistance_predict <- function(x,
                               col_ab,
                               col_date = NULL,
@@ -126,22 +128,29 @@ resistance_predict <- function(x,
                               preserve_measurements = TRUE,
                               info = interactive(),
                               ...) {
+  meet_criteria(x, allow_class = "data.frame")
+  meet_criteria(col_ab, allow_class = "character", has_length = 1, is_in = colnames(x))
+  meet_criteria(col_date, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
+  meet_criteria(year_min, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(year_max, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(year_every, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
+  meet_criteria(model, allow_class = c("character", "function"), has_length = 1, allow_NULL = TRUE)
+  meet_criteria(I_as_S, allow_class = "logical", has_length = 1)
+  meet_criteria(preserve_measurements, allow_class = "logical", has_length = 1)
+  meet_criteria(info, allow_class = "logical", has_length = 1)
  
-  stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
-  stop_if(any(dim(x) == 0), "`x` must contain rows and columns")
-  stop_if(is.null(model), 'choose a regression model with the `model` parameter, e.g. resistance_predict(..., model = "binomial")')
-  stop_ifnot(col_ab %in% colnames(x),
-             "column `", col_ab, "` not found")
-  
+  stop_if(is.null(model), 'choose a regression model with the `model` argument, e.g. resistance_predict(..., model = "binomial")')
+
  dots <- unlist(list(...))
  if (length(dots) != 0) {
-    # backwards compatibility with old parameters
-    dots.names <- dots %pm>% names()
+    # backwards compatibility with old arguments
+    dots.names <- names(dots)
    if ("tbl" %in% dots.names) {
      x <- dots[which(dots.names == "tbl")]
    }
    if ("I_as_R" %in% dots.names) {
-      warning("`I_as_R is deprecated - use I_as_S instead.", call. = FALSE)
+      warning_("`I_as_R is deprecated - use I_as_S instead.", call = FALSE)
    }
  }
  
@@ -151,7 +160,7 @@ resistance_predict <- function(x,
    stop_if(is.null(col_date), "`col_date` must be set")
  }
  stop_ifnot(col_date %in% colnames(x),
-             "column `", col_date, "` not found")
+             "column '", col_date, "' not found")
  
  # no grouped tibbles
  x <- as.data.frame(x, stringsAsFactors = FALSE)
@@ -179,12 +188,15 @@ resistance_predict <- function(x,
  # remove rows with NAs
  df <- subset(df, !is.na(df[, col_ab, drop = TRUE]))
  df$year <- year(df[, col_date, drop = TRUE])
-  df <- as.data.frame(rbind(table(df[, c("year", col_ab)])), stringsAsFactors = FALSE)
+  df <- as.data.frame(rbind(table(df[, c("year", col_ab)])),
+                      stringsAsFactors = FALSE)
  df$year <- as.integer(rownames(df))
  rownames(df) <- NULL
  
  df <- subset(df, sum(df$R + df$S, na.rm = TRUE) >= minimum)
+  # nolint start
  df_matrix <- as.matrix(df[, c("R", "S"), drop = FALSE])
+  # nolint end
  
  stop_if(NROW(df) == 0, "there are no observations")
  
@@ -300,6 +312,7 @@ rsi_predict <- resistance_predict
 #' @rdname resistance_predict
 plot.resistance_predict <- function(x, main = paste("Resistance Prediction of", x_name), ...) {
  x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
+  meet_criteria(main, allow_class = "character", has_length = 1)
  
  if (attributes(x)$I_as_S == TRUE) {
    ylab <- "%R"
@@ -336,25 +349,41 @@ plot.resistance_predict <- function(x, main = paste("Resistance Prediction of",
         col = "grey40")
 }

+
+#' @method ggplot resistance_predict
+#' @rdname resistance_predict
+# will be exported using s3_register() in R/zzz.R
+ggplot.resistance_predict <- function(x,
+                               main = paste("Resistance Prediction of", x_name),
+                               ribbon = TRUE,
+                               ...) {
+  x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
+  meet_criteria(main, allow_class = "character", has_length = 1)
+  meet_criteria(ribbon, allow_class = "logical", has_length = 1)
+  ggplot_rsi_predict(x = x, main = main, ribbon = ribbon, ...)
+}
+
 #' @rdname resistance_predict
 #' @export
 ggplot_rsi_predict <- function(x,
                               main = paste("Resistance Prediction of", x_name),
                               ribbon = TRUE,
                               ...) {
+  x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
+  meet_criteria(main, allow_class = "character", has_length = 1)
+  meet_criteria(ribbon, allow_class = "logical", has_length = 1)
  
  stop_ifnot_installed("ggplot2")
  stop_ifnot(inherits(x, "resistance_predict"), "`x` must be a resistance prediction model created with resistance_predict()")
  
-  x_name <- paste0(ab_name(attributes(x)$ab), " (", attributes(x)$ab, ")")
-  
  if (attributes(x)$I_as_S == TRUE) {
    ylab <- "%R"
  } else {
    ylab <- "%IR"
  }
  
-  p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
+  p <- ggplot2::ggplot(as.data.frame(x, stringsAsFactors = FALSE),
+                       ggplot2::aes(x = year, y = value)) +
    ggplot2::geom_point(data = subset(x, !is.na(observations)),
                        size = 2) +
    scale_y_percent(limits = c(0, 1)) +
--- a/R/rsi.R
+++ b/R/rsi.R
--- a/R/rsi_calc.R
+++ b/R/rsi_calc.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,14 +20,14 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 dots2vars <- function(...) {
  # this function is to give more informative output about 
  # variable names in count_* and proportion_* functions
  dots <- substitute(list(...))
-  paste(as.character(dots)[2:length(dots)], collapse = ", ")
+  vector_and(as.character(dots)[2:length(dots)], quotes = FALSE)
 }

 rsi_calc <- function(...,
@@ -36,10 +36,11 @@ rsi_calc <- function(...,
                     as_percent = FALSE,
                     only_all_tested = FALSE,
                     only_count = FALSE) {
-  
-  stop_ifnot(is.numeric(minimum), "`minimum` must be numeric", call = -2)
-  stop_ifnot(is.logical(as_percent), "`as_percent` must be logical", call = -2)
-  stop_ifnot(is.logical(only_all_tested), "`only_all_tested` must be logical", call = -2)
+  meet_criteria(ab_result, allow_class = c("character", "numeric", "integer"), has_length = c(1, 2, 3), .call_depth = 1)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, .call_depth = 1)
+  meet_criteria(as_percent, allow_class = "logical", has_length = 1, .call_depth = 1)
+  meet_criteria(only_all_tested, allow_class = "logical", has_length = 1, .call_depth = 1)
+  meet_criteria(only_count, allow_class = "logical", has_length = 1, .call_depth = 1)
  
  data_vars <- dots2vars(...)
  
@@ -69,7 +70,7 @@ rsi_calc <- function(...,
    }
    if (length(dots) == 0 | all(dots == "df")) {
     # for complete data.frames, like example_isolates %pm>% select(AMC, GEN) %pm>% proportion_S()
-      # and the old rsi function, which has "df" as name of the first parameter
+      # and the old rsi function, which has "df" as name of the first argument
      x <- dots_df
    } else {
      # get dots that are in column names already, and the ones that will be once evaluated using dots_df or global env
@@ -77,7 +78,7 @@ rsi_calc <- function(...,
      dots <- c(dots[dots %in% colnames(dots_df)],
                eval(parse(text = dots[!dots %in% colnames(dots_df)]), envir = dots_df, enclos = globalenv()))
      dots_not_exist <- dots[!dots %in% colnames(dots_df)]
-      stop_if(length(dots_not_exist) > 0, "column(s) not found: ", paste0("'", dots_not_exist, "'", collapse = ", "), call = -2)
+      stop_if(length(dots_not_exist) > 0, "column(s) not found: ", vector_and(dots_not_exist, quotes = TRUE), call = -2)
      x <- dots_df[, dots, drop = FALSE]
    }
  } else if (ndots == 1) {
@@ -94,8 +95,12 @@ rsi_calc <- function(...,
  }
  
  if (is.null(x)) {
-    warning("argument is NULL (check if columns exist): returning NA", call. = FALSE)
-    return(NA)
+    warning_("argument is NULL (check if columns exist): returning NA", call = FALSE)
+    if (as_percent == TRUE) {
+      return(NA_character_)
+    } else {
+      return(NA_real_)
+    }
  }
  
  print_warning <- FALSE
@@ -117,19 +122,19 @@ rsi_calc <- function(...,
      rsi_integrity_check <- as.rsi(rsi_integrity_check)
    }
    
-    x_transposed <- as.list(as.data.frame(t(x)))
+    x_transposed <- as.list(as.data.frame(t(x), stringsAsFactors = FALSE))
    if (only_all_tested == TRUE) {
      # no NAs in any column
      y <- apply(X = as.data.frame(lapply(x, as.integer), stringsAsFactors = FALSE),
                 MARGIN = 1,
                 FUN = min)
      numerator <- sum(as.integer(y) %in% as.integer(ab_result), na.rm = TRUE)
-      denominator <- sum(sapply(x_transposed, function(y) !(any(is.na(y)))))
+      denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(any(is.na(y)))))
    } else {
      # may contain NAs in any column
      other_values <- setdiff(c(NA, levels(ab_result)), ab_result)
-      numerator <- sum(sapply(x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
-      denominator <- sum(sapply(x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
+      numerator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
+      denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
    }
  } else {
    # x is not a data.frame
@@ -142,8 +147,13 @@ rsi_calc <- function(...,
  }
  
  if (print_warning == TRUE) {
-    warning("Increase speed by transforming to class <rsi> on beforehand: your_data %pm>% mutate_if(is.rsi.eligible, as.rsi)",
-            call. = FALSE)
+    if (message_not_thrown_before("rsi_calc")) {
+      warning_("Increase speed by transforming to class <rsi> on beforehand:\n",
+               "  your_data %>% mutate_if(is.rsi.eligible, as.rsi)\n",
+               "  your_data %>% mutate(across(where(is.rsi.eligible), as.rsi))",
+               call = FALSE)
+      remember_thrown_message("rsi_calc")
+    }
  }
  
  if (only_count == TRUE) {
@@ -154,7 +164,7 @@ rsi_calc <- function(...,
    if (data_vars != "") {
      data_vars <- paste(" for", data_vars)
    }
-    warning("Introducing NA: only ", denominator, " results available", data_vars, " (`minimum` = ", minimum, ").", call. = FALSE)
+    warning_("Introducing NA: only ", denominator, " results available", data_vars, " (`minimum` = ", minimum, ").", call = FALSE)
    fraction <- NA_real_
  } else {
    fraction <- numerator / denominator
@@ -177,17 +187,21 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
                        combine_SI = TRUE,
                        combine_IR = FALSE,
                        combine_SI_missing = FALSE) {
+  meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1, .call_depth = 1)
+  meet_criteria(data, allow_class = "data.frame", contains_column_class = "rsi", .call_depth = 1)
+  meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE, .call_depth = 1)
+  meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE, .call_depth = 1)
+  meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, .call_depth = 1)
+  meet_criteria(as_percent, allow_class = "logical", has_length = 1, .call_depth = 1)
+  meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = 1)
+  meet_criteria(combine_SI_missing, allow_class = "logical", has_length = 1, .call_depth = 1)
  
  check_dataset_integrity()
-  stop_ifnot(is.data.frame(data), "`data` must be a data.frame", call = -2)
-  stop_if(any(dim(data) == 0), "`data` must contain rows and columns", call = -2)
-  stop_ifnot(any(sapply(data, is.rsi), na.rm = TRUE), "no columns with class <rsi> found. See ?as.rsi.", call = -2)
+  
  if (isTRUE(combine_IR) & isTRUE(combine_SI_missing)) {
    combine_SI <- FALSE
  }
  stop_if(isTRUE(combine_SI) & isTRUE(combine_IR), "either `combine_SI` or `combine_IR` can be TRUE, not both", call = -2)
-  stop_ifnot(is.numeric(minimum), "`minimum` must be numeric", call = -2)
-  stop_ifnot(is.logical(as_percent), "`as_percent` must be logical", call = -2)
  
  translate_ab <- get_translate_ab(translate_ab)
  
@@ -195,10 +209,10 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
  if (inherits(data, "grouped_df")) {
    data_has_groups <- TRUE
    groups <- setdiff(names(attributes(data)$groups), ".rows")
-    data <- data[, c(groups, colnames(data)[sapply(data, is.rsi)]), drop = FALSE]
+    data <- data[, c(groups, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.rsi)]), drop = FALSE]
  } else {
    data_has_groups <- FALSE
-    data <- data[, colnames(data)[sapply(data, is.rsi)], drop = FALSE]
+    data <- data[, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.rsi)], drop = FALSE]
  }
  
  data <- as.data.frame(data, stringsAsFactors = FALSE)
@@ -235,7 +249,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
      } else {
        values <- factor(values, levels = c("S", "I", "R"), ordered = TRUE)
      }
-      col_results <- as.data.frame(as.matrix(table(values)))
+      col_results <- as.data.frame(as.matrix(table(values)), stringsAsFactors = FALSE)
      col_results$interpretation <- rownames(col_results)
      col_results$isolates <- col_results[, 1, drop = TRUE]
      if (NROW(col_results) > 0 && sum(col_results$isolates, na.rm = TRUE) > 0) {
@@ -260,7 +274,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
          }
          out_new <- cbind(group_values, out_new)
        }
-        out <- rbind(out, out_new)
+        out <- rbind(out, out_new, stringsAsFactors = FALSE)
      }
    }
    out
--- a/R/rsi_df.R
+++ b/R/rsi_df.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' @rdname proportion 
@@ -32,7 +32,6 @@ rsi_df <- function(data,
                   as_percent = FALSE,
                   combine_SI = TRUE,
                   combine_IR = FALSE) {
-  
  rsi_calc_df(type = "both",
              data = data,
              translate_ab = translate_ab,
--- a/R/skewness.R
+++ b/R/skewness.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,22 +20,23 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Skewness of the sample
+#' Skewness of the Sample
 #'
 #' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
 #'
 #' When negative ('left-skewed'): the left tail is longer; the mass of the distribution is concentrated on the right of a histogram. When positive ('right-skewed'): the right tail is longer; the mass of the distribution is concentrated on the left of a histogram. A normal distribution has a skewness of 0.
-#' @inheritSection lifecycle Stable lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @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
+#' @param na.rm a [logical] value indicating whether `NA` values should be stripped before the computation proceeds
 #' @seealso [kurtosis()]
 #' @rdname skewness
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @export
 skewness <- function(x, na.rm = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
  UseMethod("skewness")
 }

@@ -43,6 +44,7 @@ skewness <- function(x, na.rm = FALSE) {
 #' @rdname skewness
 #' @export
 skewness.default <- function(x, na.rm = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
  x <- as.vector(x)
  if (na.rm == TRUE) {
    x <- x[!is.na(x)]
@@ -55,6 +57,7 @@ skewness.default <- function(x, na.rm = FALSE) {
 #' @rdname skewness
 #' @export
 skewness.matrix <- function(x, na.rm = FALSE) {
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
  apply(x, 2, skewness.default, na.rm = na.rm)
 }

@@ -62,5 +65,6 @@ skewness.matrix <- function(x, na.rm = FALSE) {
 #' @rdname skewness
 #' @export
 skewness.data.frame <- function(x, na.rm = FALSE) {
-  sapply(x, skewness.default, na.rm = na.rm)
+  meet_criteria(na.rm, allow_class = "logical", has_length = 1)
+  vapply(FUN.VALUE = double(1), x, skewness.default, na.rm = na.rm)
 }
--- a/R/sysdata.rda
+++ b/R/sysdata.rda
--- a/R/translate.R
+++ b/R/translate.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,32 +20,32 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-#' Translate strings from AMR package
+#' Translate Strings from AMR Package
 #'
 #' For language-dependent output of AMR functions, like [mo_name()], [mo_gramstain()], [mo_type()] and [ab_name()].
-#' @inheritSection lifecycle Stable lifecycle
-#' @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: <https://github.com/msberends/AMR/blob/master/data-raw/translations.tsv>. This file will be read by all functions where a translated output can be desired, like all [mo_property()] functions ([mo_name()], [mo_gramstain()], [mo_type()], etc.) and [ab_property()] functions ([ab_name()], [ab_group()] etc.). 
+#' @inheritSection lifecycle Stable Lifecycle
+#' @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: <https://github.com/msberends/AMR/blob/master/data-raw/translations.tsv>. This file will be read by all functions where a translated output can be desired, like all [`mo_*`][mo_property()] functions (such as [mo_name()], [mo_gramstain()], [mo_type()], etc.) and [`ab_*`][ab_property()] functions (such as [ab_name()], [ab_group()], etc.). 
 #'
-#' Currently supported languages are: `r paste(sort(gsub(";.*", "", ISOcodes::ISO_639_2[which(ISOcodes::ISO_639_2$Alpha_2 %in% LANGUAGES_SUPPORTED), "Name"])), collapse = ", ")`. Please note that currently not all these languages have translations available for all antimicrobial agents and colloquial microorganism names. 
+#' Currently supported languages are: `r vector_and(gsub(";.*", "", ISOcodes::ISO_639_2[which(ISOcodes::ISO_639_2$Alpha_2 %in% LANGUAGES_SUPPORTED), "Name"]), quotes = FALSE)`. Please note that currently not all these languages have translations available for all antimicrobial agents and colloquial microorganism names. 
 #'
 #' Please suggest your own translations [by creating a new issue on our repository](https://github.com/msberends/AMR/issues/new?title=Translations).
 #'
-#' ## Changing the default language
+#' ## Changing the Default Language
 #' The system language will be used at default (as returned by `Sys.getenv("LANG")` or, if `LANG` is not set, [Sys.getlocale()]), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:
 #' 
 #'   1. Setting the R option `AMR_locale`, e.g. by running `options(AMR_locale = "de")`
 #'   2. Setting the system variable `LANGUAGE` or `LANG`, e.g. by adding `LANGUAGE="de_DE.utf8"` to your `.Renviron` file in your home directory
 #' 
 #' So if the R option `AMR_locale` is set, the system variables `LANGUAGE` and `LANG` will be ignored.
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @rdname translate
 #' @name translate
 #' @export
 #' @examples
-#' # The 'language' parameter of below functions
+#' # The 'language' argument of below functions
 #' # will be set automatically to your system language
 #' # with get_locale()
 #'
@@ -73,7 +73,7 @@
 #' mo_name("CoNS", language = "pt")
 #' #> "Staphylococcus coagulase negativo (CoNS)"
 get_locale <- function() {
-  # AMR versions prior to 1.3.0 used the environmental variable:
+  # AMR versions 1.3.0 and prior used the environmental variable:
  if (!identical("", Sys.getenv("AMR_locale"))) {
    options(AMR_locale = Sys.getenv("AMR_locale"))
  }
@@ -83,8 +83,8 @@ get_locale <- function() {
    if (lang %in% LANGUAGES_SUPPORTED) {
      return(lang)
    } else {
-      stop_("unsupported language set as option 'AMR_locale': '", lang, "' - use one of: ",
-            paste0("'", LANGUAGES_SUPPORTED, "'", collapse = ", "))
+      stop_("unsupported language set as option 'AMR_locale': \"", lang, "\" - use either ",
+            vector_or(LANGUAGES_SUPPORTED, quotes = TRUE))
    }
  } else {
    # we now support the LANGUAGE system variable - return it if set
@@ -96,25 +96,25 @@ get_locale <- function() {
    }
  }
  
-  coerce_language_setting(Sys.getlocale())
+  coerce_language_setting(Sys.getlocale("LC_COLLATE"))
 }

 coerce_language_setting <- function(lang) {
  # grepl() with ignore.case = FALSE is faster than %like%
-  if (grepl("^(English|en_|EN_)", lang, ignore.case = FALSE)) {
+  if (grepl("^(English|en_|EN_)", lang, ignore.case = FALSE, perl = TRUE)) {
    # as first option to optimise speed
    "en"
-  } else if (grepl("^(German|Deutsch|de_|DE_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(German|Deutsch|de_|DE_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "de"
-  } else if (grepl("^(Dutch|Nederlands|nl_|NL_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(Dutch|Nederlands|nl_|NL_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "nl"
-  } else if (grepl("^(Spanish|Espa.+ol|es_|ES_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(Spanish|Espa.+ol|es_|ES_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "es"
-  } else if (grepl("^(Italian|Italiano|it_|IT_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(Italian|Italiano|it_|IT_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "it"
-  } else if (grepl("^(French|Fran.+ais|fr_|FR_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(French|Fran.+ais|fr_|FR_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "fr"
-  } else if (grepl("^(Portuguese|Portugu.+s|pt_|PT_)", lang, ignore.case = FALSE)) {
+  } else if (grepl("^(Portuguese|Portugu.+s|pt_|PT_)", lang, ignore.case = FALSE, perl = TRUE)) {
    "pt"
  } else {
    # other language -> set to English
@@ -123,7 +123,11 @@ coerce_language_setting <- function(lang) {
 }

 # translate strings based on inst/translations.tsv
-translate_AMR <- function(from, language = get_locale(), only_unknown = FALSE) {
+translate_AMR <- function(from,
+                          language = get_locale(), 
+                          only_unknown = FALSE,
+                          only_affect_ab_names = FALSE,
+                          only_affect_mo_names = FALSE) {
  
  if (is.null(language)) {
    return(from)
@@ -138,36 +142,51 @@ translate_AMR <- function(from, language = get_locale(), only_unknown = FALSE) {
  from_unique_translated <- from_unique
  
  stop_ifnot(language %in% LANGUAGES_SUPPORTED,
-             "unsupported language: '", language, "' - use one of: ",
-             paste0("'", LANGUAGES_SUPPORTED, "'", collapse = ", "),
+             "unsupported language: \"", language, "\" - use either ",
+             vector_or(LANGUAGES_SUPPORTED, quotes = TRUE),
             call = FALSE)
  
-  df_trans <- subset(df_trans, lang == language)
+  # only keep lines where translation is available for this language
+  df_trans <- df_trans[which(!is.na(df_trans[, language, drop = TRUE])), , drop = FALSE]
+  # and where the original string is not equal to the string in the target language
+  df_trans <- df_trans[which(df_trans[, "pattern", drop = TRUE] != df_trans[, language, drop = TRUE]), , drop = FALSE]
  if (only_unknown == TRUE) {
    df_trans <- subset(df_trans, pattern %like% "unknown")
  }
+  if (only_affect_ab_names == TRUE) {
+    df_trans <- subset(df_trans, affect_ab_name == TRUE)
+  }
+  if (only_affect_mo_names == TRUE) {
+    df_trans <- subset(df_trans, affect_mo_name == TRUE)
+  }
+  if (NROW(df_trans) == 0) {
+    return(from)
+  }
  
-  # default case sensitive if value if 'ignore.case' is missing:
-  df_trans$ignore.case[is.na(df_trans$ignore.case)] <- FALSE
-  # default not using regular expressions (fixed = TRUE) if 'fixed' is missing:
-  df_trans$fixed[is.na(df_trans$fixed)] <- TRUE
+  # default: case sensitive if value if 'case_sensitive' is missing:
+  df_trans$case_sensitive[is.na(df_trans$case_sensitive)] <- TRUE
+  # default: not using regular expressions if 'regular_expr' is missing:
+  df_trans$regular_expr[is.na(df_trans$regular_expr)] <- FALSE
  
  # check if text to look for is in one of the patterns
-  any_form_in_patterns <- tryCatch(any(from_unique %like% paste0("(", paste(df_trans$pattern, collapse = "|"), ")")),
-                                   error = function(e) {
-                                     warning("Translation not possible. Please open an issue on GitHub (https://github.com/msberends/AMR/issues).", call. = FALSE)
-                                     return(FALSE)
-                                   })
+  any_form_in_patterns <- tryCatch(
+    any(from_unique %like% paste0("(", paste(gsub(" +\\(.*", "", df_trans$pattern), collapse = "|"), ")")),
+    error = function(e) {
+      warning_("Translation not possible. Please open an issue on GitHub (https://github.com/msberends/AMR/issues).", call = FALSE)
+      return(FALSE)
+    })
+  
  if (NROW(df_trans) == 0 | !any_form_in_patterns) {
    return(from)
  }
  
  lapply(seq_len(nrow(df_trans)), 
         function(i) from_unique_translated <<- gsub(pattern = df_trans$pattern[i],
-                                                     replacement = df_trans$replacement[i],
+                                                     replacement = df_trans[i, language, drop = TRUE],
                                                     x = from_unique_translated,
-                                                     ignore.case = df_trans$ignore.case[i], 
-                                                     fixed = df_trans$fixed[i]))
+                                                     ignore.case = !df_trans$case_sensitive[i] & df_trans$regular_expr[i], 
+                                                     fixed = !df_trans$regular_expr[i],
+                                                     perl = df_trans$regular_expr[i]))
  
  # force UTF-8 for diacritics
  from_unique_translated <- enc2utf8(from_unique_translated)
--- a/R/whocc.R
+++ b/R/whocc.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 #' WHOCC: WHO Collaborating Centre for Drug Statistics Methodology
@@ -28,14 +28,14 @@
 #' 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 **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>). 
+#' 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 (<https://ec.europa.eu/health/documents/community-register/html/reg_hum_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/.>
-#' @inheritSection AMR Read more on our website!
+#' @inheritSection AMR Read more on Our Website!
 #' @name WHOCC
 #' @rdname WHOCC
 #' @examples
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,34 +20,25 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

-.onLoad <- function(libname, pkgname) {
-  assign(x = "AB_lookup",
-         value = create_AB_lookup(),
-         envir = asNamespace("AMR"))
-  
-  assign(x = "MO_lookup",
-         value = create_MO_lookup(),
-         envir = asNamespace("AMR"))
-  
-  assign(x = "MO.old_lookup",
-         value = create_MO.old_lookup(),
-         envir = asNamespace("AMR"))
-  
-  assign(x = "LANGUAGES_SUPPORTED",
-         value = sort(c("en", unique(translations_file$lang))),
-         envir = asNamespace("AMR"))
-  
-  assign(x = "MO_CONS",
-         value = create_species_cons_cops("CoNS"),
-         envir = asNamespace("AMR"))
-  
-  assign(x = "MO_COPS",
-         value = create_species_cons_cops("CoPS"),
-         envir = asNamespace("AMR"))
+# set up package environment, used by numerous AMR functions
+pkg_env <- new.env(hash = FALSE)
+pkg_env$mo_failed <- character(0)

+# determine info icon for messages
+utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)
+is_latex <- tryCatch(import_fn("is_latex_output", "knitr", error_on_fail = FALSE)(),
+                     error = function(e) FALSE)
+if (utf8_supported && !is_latex) {
+  # \u2139 is a symbol officially named 'information source'
+  pkg_env$info_icon <- "\u2139"
+} else {
+  pkg_env$info_icon <- "i"
+}
+
+.onLoad <- function(libname, pkgname) {
  # Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
  # without the need to depend on other packages. This was suggested by the 
  # developers of the vctrs package: 
@@ -70,94 +61,15 @@
  s3_register("skimr::get_skimmers", "rsi")
  s3_register("skimr::get_skimmers", "mic")
  s3_register("skimr::get_skimmers", "disk")
-}
-
-.onAttach <- function(...) {
-  # show notice in 10% of cases in interactive session
-  if (!interactive() || stats::runif(1) > 0.1 || isTRUE(as.logical(getOption("AMR_silentstart", FALSE)))) {
-    return()
-  }
-  packageStartupMessage("Thank you for using the AMR package! ",
-                        "If you have a minute, please anonymously fill in this short questionnaire to improve the package and its functionalities:",
-                        "\nhttps://msberends.github.io/AMR/survey.html",
-                        "\n[ prevent his notice with suppressPackageStartupMessages(library(AMR)) or use options(AMR_silentstart = TRUE) ]")
-}
-
-create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
-  # Determination of which staphylococcal species are CoNS/CoPS according to Becker et al.:
-  # https://cmr.asm.org/content/cmr/27/4/870/F6.large.jpg
-  # this function returns class <mo>
-  MO_staph <- AMR::microorganisms
-  MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
-  if (type == "CoNS") {
-    MO_staph[which(MO_staph$species %in% c("coagulase-negative",
-                                           "arlettae", "auricularis", "capitis",
-                                           "caprae", "carnosus", "chromogenes", "cohnii", "condimenti",
-                                           "devriesei", "epidermidis", "equorum", "felis",
-                                           "fleurettii", "gallinarum", "haemolyticus",
-                                           "hominis", "jettensis", "kloosii", "lentus",
-                                           "lugdunensis", "massiliensis", "microti",
-                                           "muscae", "nepalensis", "pasteuri", "petrasii",
-                                           "pettenkoferi", "piscifermentans", "rostri",
-                                           "saccharolyticus", "saprophyticus", "sciuri",
-                                           "stepanovicii", "simulans", "succinus",
-                                           "vitulinus", "warneri", "xylosus")
-                   | (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
-             "mo", drop = TRUE]
-  } else if (type == "CoPS") {
-    MO_staph[which(MO_staph$species %in% c("coagulase-positive",
-                                           "simiae", "agnetis",
-                                           "delphini", "lutrae",
-                                           "hyicus", "intermedius",
-                                           "pseudintermedius", "pseudointermedius",
-                                           "schweitzeri", "argenteus")
-                   | (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
-             "mo", drop = TRUE]
-  }
-}
-
-create_AB_lookup <- function() {
-  AB_lookup <- AMR::antibiotics
-  AB_lookup$generalised_name <- generalise_antibiotic_name(AB_lookup$name)
-  AB_lookup$generalised_synonyms <- lapply(AB_lookup$synonyms, generalise_antibiotic_name)
-  AB_lookup$generalised_abbreviations <- lapply(AB_lookup$abbreviations, generalise_antibiotic_name)
-  AB_lookup$generalised_loinc <- lapply(AB_lookup$loinc, generalise_antibiotic_name)
-  AB_lookup
-}
-
-create_MO_lookup <- function() {
-  MO_lookup <- AMR::microorganisms
+  s3_register("ggplot2::ggplot", "rsi")
+  s3_register("ggplot2::ggplot", "mic")
+  s3_register("ggplot2::ggplot", "disk")
+  s3_register("ggplot2::ggplot", "resistance_predict")
  
-  MO_lookup$kingdom_index <- NA_real_
-  MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
-  MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
-  MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
-  MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
-  # all the rest
-  MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
-  
-  # use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
-  MO_lookup$fullname_lower <- tolower(trimws(paste(MO_lookup$genus, 
-                                                   MO_lookup$species,
-                                                   MO_lookup$subspecies)))
-  ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname)
-  MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname"])
-  MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
-  
-  # add a column with only "e coli" like combinations
-  MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
-  
-  # so arrange data on prevalence first, then kingdom, then full name
-  MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), ]
-}
-
-create_MO.old_lookup <- function() {
-  MO.old_lookup <- AMR::microorganisms.old
-  MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
-  
-  # add a column with only "e coli"-like combinations
-  MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
-  
-  # so arrange data on prevalence first, then full name
-  MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
+  # if mo source exists, fire it up (see mo_source())
+  try({
+    if (file.exists(getOption("AMR_mo_source", "~/mo_source.rds"))) {
+      invisible(get_mo_source())
+    }
+  }, silent = TRUE)
 }
--- a/README.md
+++ b/README.md
@@ -2,24 +2,31 @@

 # `AMR` (for R)

-<img src="https://www.r-pkg.org/badges/version-ago/AMR" />
-<img src="https://cranlogs.r-pkg.org/badges/grand-total/AMR" />
+[![CRAN](https://www.r-pkg.org/badges/version-ago/AMR)](https://cran.r-project.org/package=AMR)
+[![CRANlogs](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](https://cran.r-project.org/package=AMR)
+![R-code-check](https://github.com/msberends/AMR/workflows/R-code-check/badge.svg?branch=master)
+[![CodeFactor](https://www.codefactor.io/repository/github/msberends/amr/badge)](https://www.codefactor.io/repository/github/msberends/amr)
+[![Codecov](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR?branch=master)

 <img src="https://msberends.github.io/AMR/works_great_on.png" align="center" height="150px" />

-`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
- 
-After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+The latest built **source package** (`AMR_latest.tar.gz`) can be found in folder [/data-raw/](https://github.com/msberends/AMR/tree/master/data-raw).

-This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
+`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 150 countries.
+ 
+After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+
+This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.

 This is the development source of the `AMR` package for R. Not a developer? Then please visit our website [https://msberends.github.io/AMR/](https://msberends.github.io/AMR/) to read more about this package.

-*NOTE: this source code is on GitHub (https://github.com/msberends/AMR), but also automatically mirrored to GitLab (https://gitlab.com/msberends/AMR).*
+*NOTE: this source code is on GitHub (https://github.com/msberends/AMR), but also automatically mirrored to our university's Gitea server (https://git.web.rug.nl/P281424/AMR) and to GitLab (https://gitlab.com/msberends/AMR).*

 ### How to get this package
 Please see [our website](https://msberends.github.io/AMR/#get-this-package).

+Bottom line: `install.packages("AMR")`
+
 ### Copyright

 This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 title: "AMR (for R)"
@@ -99,18 +99,19 @@ reference:
      for more information about how to work with functions in this package.
    contents:
    - "`AMR`"
+    - "`example_isolates`"
+    - "`microorganisms`"
+    - "`microorganisms.codes`"
+    - "`microorganisms.old`"
+    - "`antibiotics`"
+    - "`intrinsic_resistant`"
+    - "`dosage`"
    - "`catalogue_of_life`"
    - "`catalogue_of_life_version`"
    - "`WHOCC`"
    - "`lifecycle`"
-    - "`microorganisms`"
-    - "`antibiotics`"
-    - "`intrinsic_resistant`"
-    - "`example_isolates`"
    - "`example_isolates_unclean`"
    - "`rsi_translation`"
-    - "`microorganisms.codes`"
-    - "`microorganisms.old`"
    - "`WHONET`"
    
  - title: "Preparing data: microorganisms"
@@ -142,24 +143,25 @@ reference:
    - "`as.mic`"
    - "`as.disk`"
    - "`eucast_rules`"
-    - "`plot`"
+    - "`custom_eucast_rules`"
    
  - title: "Analysing data: antimicrobial resistance"
    desc: >
      Use these function for the analysis part. You can use `susceptibility()` or `resistance()` on any antibiotic column.
-      Be sure to first select the isolates that are appropiate for analysis, by using `first_isolate()`.
-      You can also filter your data on certain resistance in certain antibiotic classes (`filter_ab_class()`), or determine multi-drug resistant microorganisms (MDRO, `mdro()`).
+      Be sure to first select the isolates that are appropiate for analysis, by using `first_isolate()` or `is_new_episode()`.
+      You can also filter your data on certain resistance in certain antibiotic classes (`carbapenems()`, `aminoglycosides()`), or determine multi-drug resistant microorganisms (MDRO, `mdro()`).
    contents:
    - "`proportion`"
    - "`count`"
+    - "`is_new_episode`"
    - "`first_isolate`"
-    - "`key_antibiotics`"
+    - "`key_antimicrobials`"
    - "`mdro`"
    - "`count`"
+    - "`plot`"
    - "`ggplot_rsi`"
    - "`bug_drug_combinations`"
    - "`antibiotic_class_selectors`"
-    - "`filter_ab_class`"
    - "`resistance_predict`"
    - "`guess_ab_col`"
    
@@ -174,27 +176,28 @@ reference:
    - "`availability`"
    - "`get_locale`"
    - "`ggplot_pca`"
+    - "`italicise_taxonomy`"
    - "`join`"
    - "`like`"
    - "`mo_matching_score`"
    - "`pca`"
+    - "`random`"
  
  - title: "Other: statistical tests"
    desc: >
-      Some statistical tests or methods are not part of base R and are added to this package for convenience.
+      Some statistical tests or methods are not part of base R and were added to this package for convenience.
    contents:
    - "`g.test`"
    - "`kurtosis`"
    - "`skewness`"
-    - "`p_symbol`"

-  # - title: "Other: deprecated functions"
-  #   desc: >
-  #     These functions are deprecated, meaning that they will still
-  #     work but show a warning with every use and will be removed
-  #     in a future version.
-  #   contents:
-  #   - "`AMR-deprecated`"
+  - title: "Other: deprecated functions"
+    desc: >
+      These functions are deprecated, meaning that they will still
+      work but show a warning with every use and will be removed
+      in a future version.
+    contents:
+    - "`AMR-deprecated`"

 authors:
  Matthijs S. Berends:
@@ -211,6 +214,8 @@ authors:
    href: https://www.rug.nl/staff/c.glasner/

 template:
+  # this requires the 'preferably' package, https://github.com/amirmasoudabdol/preferably/
+  # package: preferably
  assets: "pkgdown/logos" # use logos in this folder
  params:
    noindex: false
--- a/codecov.yml
+++ b/codecov.yml
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 codecov:
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,3 +1,3 @@
-* Edited the unit tests, so they will run under 10 minutes on CRAN (using testthat::skip_on_cran() on some tests).
+* This package has been archived on 22 May 2021 because of errors in the dplyr package, causing the skimr package to fail: <https://github.com/tidyverse/dplyr/issues/5881>. This AMR package contains a fix around this error. Perhaps an idea for future development of CRAN to send an automated email to a maintainer with a warning that a package will be archived in due time?

-* Since version 0.3.0 (2018-08-14), CHECK returns a NOTE for having a data directory over 3 MB. This is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package.
+* This package continuously has a tarball size of over 7 MB and an installation size of over 5 MB, which will return a NOTE on R CMD CHECK. This has been the case in the last releases as well. The package size is needed to offer users reference data for the complete taxonomy of microorganisms - one of the most important features of this package. This was written and explained in a manuscript that was accepted for publication in the Journal of Statistical Software earlier this year. We will add the paper as a vignette after publication in a next version. All data sets were compressed using `compression = "xz"` to make them as small as possible.
--- a/data-raw/AMR_latest.tar.gz
+++ b/data-raw/AMR_latest.tar.gz
--- a/data-raw/Dosages_v_11.0_Breakpoint_Tables.xlsx
+++ b/data-raw/Dosages_v_11.0_Breakpoint_Tables.xlsx
--- a/data-raw/SNOMED_PHVS_Microorganism_CDC_V12.txt
+++ b/data-raw/SNOMED_PHVS_Microorganism_CDC_V12.txt
--- a/data-raw/_install_deps.R
+++ b/data-raw/_install_deps.R
@@ -0,0 +1,56 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+# some old R instances have trouble installing tinytest, so we ship it too
+install.packages("data-raw/tinytest_1.2.4.10.tar.gz")
+install.packages("data-raw/AMR_latest.tar.gz", dependencies = FALSE)
+
+pkg_suggests <- gsub("[^a-zA-Z0-9]+", "", unlist(strsplit(packageDescription("AMR", fields = "Suggests"), ", ?")))
+cat("Packages listed in Suggests:", paste(pkg_suggests, collapse = ", "), "\n")
+
+to_install <- pkg_suggests[!pkg_suggests %in% rownames(utils::installed.packages())]
+if (length(to_install) == 0) {
+  message("\nNothing to install\n")
+}
+for (i in seq_len(length(to_install))) {
+  cat("Installing package", to_install[i], "\n")
+  tryCatch(install.packages(to_install[i], repos = "https://cran.rstudio.com/", dependencies = TRUE, quiet = TRUE),
+           # message = function(m) invisible(),
+           warning = function(w) message(w$message),
+           error = function(e) message(e$message))
+}
+
+to_update <- as.data.frame(utils::old.packages(repos = "https://cran.rstudio.com/"), stringsAsFactors = FALSE)
+to_update <- to_update[which(to_update$Package %in% pkg_suggests), "Package", drop = TRUE]
+if (length(to_update) == 0) {
+  message("\nNothing to update\n")
+}
+for (i in seq_len(length(to_update))) {
+  cat("Updating package", to_update[i], "\n")
+  tryCatch(update.packages(to_update[i], repos = "https://cran.rstudio.com/", ask = FALSE),
+           # message = function(m) invisible(),
+           warning = function(w) message(w$message),
+           error = function(e) message(e$message))
+}
--- a/data-raw/_internals.R
+++ b/data-raw/_internals.R
@@ -0,0 +1,360 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
+#                                                                      #
+# SOURCE                                                               #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# LICENCE                                                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
+# Developed at the University of Groningen, the Netherlands, in        #
+# collaboration with non-profit organisations Certe Medical            #
+# Diagnostics & Advice, and University Medical Center Groningen.       # 
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
+# ==================================================================== #
+
+# Run this file to update the package using:
+# source("data-raw/_internals.R")
+
+library(dplyr, warn.conflicts = FALSE)
+devtools::load_all(quiet = TRUE)
+
+old_globalenv <- ls(envir = globalenv())
+
+# Helper functions --------------------------------------------------------
+
+create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
+  # Determination of which staphylococcal species are CoNS/CoPS according to:
+  # - Becker et al. 2014, PMID 25278577
+  # - Becker et al. 2019, PMID 30872103
+  # - Becker et al. 2020, PMID 32056452
+  # this function returns class <mo>
+  MO_staph <- AMR::microorganisms
+  MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
+  if (type == "CoNS") {
+    MO_staph[which(MO_staph$species %in% c("coagulase-negative", "argensis", "arlettae",
+                                           "auricularis", "borealis", "caeli", "capitis", "caprae", 
+                                           "carnosus", "casei", "chromogenes", "cohnii", "condimenti",
+                                           "croceilyticus",
+                                           "debuckii", "devriesei", "edaphicus", "epidermidis",
+                                           "equorum", "felis", "fleurettii", "gallinarum",
+                                           "haemolyticus", "hominis", "jettensis", "kloosii",
+                                           "lentus", "lugdunensis", "massiliensis", "microti",
+                                           "muscae", "nepalensis", "pasteuri", "petrasii",
+                                           "pettenkoferi", "piscifermentans", "pragensis", "pseudoxylosus",
+                                           "pulvereri", "rostri", "saccharolyticus", "saprophyticus",
+                                           "sciuri", "simulans", "stepanovicii", "succinus",
+                                           "ureilyticus",
+                                           "vitulinus", "vitulus", "warneri", "xylosus")
+                   | (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
+             "mo", drop = TRUE]
+  } else if (type == "CoPS") {
+    MO_staph[which(MO_staph$species %in% c("coagulase-positive", "coagulans",
+                                           "agnetis", "argenteus",
+                                           "cornubiensis",
+                                           "delphini", "lutrae",
+                                           "hyicus", "intermedius",
+                                           "pseudintermedius", "pseudointermedius",
+                                           "schweitzeri", "simiae")
+                   | (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
+             "mo", drop = TRUE]
+  }
+}
+
+create_AB_lookup <- function() {
+  AB_lookup <- AMR::antibiotics
+  AB_lookup$generalised_name <- generalise_antibiotic_name(AB_lookup$name)
+  AB_lookup$generalised_synonyms <- lapply(AB_lookup$synonyms, generalise_antibiotic_name)
+  AB_lookup$generalised_abbreviations <- lapply(AB_lookup$abbreviations, generalise_antibiotic_name)
+  AB_lookup$generalised_loinc <- lapply(AB_lookup$loinc, generalise_antibiotic_name)
+  AB_lookup$generalised_all <- unname(lapply(as.list(as.data.frame(t(AB_lookup[, 
+                                                                               c("ab", "atc", "cid", "name",
+                                                                                 colnames(AB_lookup)[colnames(AB_lookup) %like% "generalised"]),
+                                                                               drop = FALSE]),
+                                                                   stringsAsFactors = FALSE)),
+                                             function(x) {
+                                               x <- generalise_antibiotic_name(unname(unlist(x)))
+                                               x[x != ""]
+                                             }))
+  AB_lookup
+}
+
+create_MO_lookup <- function() {
+  MO_lookup <- AMR::microorganisms
+  
+  MO_lookup$kingdom_index <- NA_real_
+  MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
+  MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
+  MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
+  MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
+  # all the rest
+  MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
+  
+  # use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
+  MO_lookup$fullname_lower <- tolower(trimws(paste(MO_lookup$genus, 
+                                                   MO_lookup$species,
+                                                   MO_lookup$subspecies)))
+  ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname)
+  MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname"])
+  MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
+  
+  # add a column with only "e coli" like combinations
+  MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
+  
+  # so arrange data on prevalence first, then kingdom, then full name
+  MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), ]
+}
+
+create_MO.old_lookup <- function() {
+  MO.old_lookup <- AMR::microorganisms.old
+  MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
+  
+  # add a column with only "e coli"-like combinations
+  MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
+  
+  # so arrange data on prevalence first, then full name
+  MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
+}
+
+create_intr_resistance <- function() {
+  # for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
+  paste(AMR::microorganisms[match(AMR::intrinsic_resistant$microorganism, AMR::microorganisms$fullname), "mo", drop = TRUE],
+        AMR::antibiotics[match(AMR::intrinsic_resistant$antibiotic, AMR::antibiotics$name), "ab", drop = TRUE])
+}
+
+
+
+# Save internal data to R/sysdata.rda -------------------------------------
+
+# See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
+eucast_rules_file <- utils::read.delim(file = "data-raw/eucast_rules.tsv",
+                                       skip = 10,
+                                       sep = "\t",
+                                       stringsAsFactors = FALSE,
+                                       header = TRUE,
+                                       strip.white = TRUE,
+                                       na = c(NA, "", NULL)) %>% 
+  # take the order of the reference.rule_group column in the original data file
+  mutate(reference.rule_group = factor(reference.rule_group,
+                                       levels = unique(reference.rule_group),
+                                       ordered = TRUE),
+         sorting_rule = ifelse(grepl("^Table", reference.rule, ignore.case = TRUE), 1, 2)) %>% 
+  arrange(reference.rule_group,
+          reference.version,
+          sorting_rule,
+          reference.rule) %>% 
+  mutate(reference.rule_group = as.character(reference.rule_group)) %>% 
+  select(-sorting_rule)
+
+# Translations
+translations_file <- utils::read.delim(file = "data-raw/translations.tsv",
+                                       sep = "\t",
+                                       stringsAsFactors = FALSE,
+                                       header = TRUE,
+                                       blank.lines.skip = TRUE,
+                                       fill = TRUE,
+                                       strip.white = TRUE,
+                                       encoding = "UTF-8",
+                                       fileEncoding = "UTF-8",
+                                       na.strings = c(NA, "", NULL),
+                                       allowEscapes = TRUE, # else "\\1" will be imported as "\\\\1"
+                                       quote = "")
+
+# Old microorganism codes
+microorganisms.translation <- readRDS("data-raw/microorganisms.translation.rds")
+
+# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
+INTRINSIC_R <- create_intr_resistance()
+
+# for checking input in `language` argument in e.g. mo_*() and ab_*() functions
+LANGUAGES_SUPPORTED <- sort(c("en", colnames(translations_file)[nchar(colnames(translations_file)) == 2]))
+
+# vectors of CoNS and CoPS, improves speed in as.mo()
+MO_CONS <- create_species_cons_cops("CoNS")
+MO_COPS <- create_species_cons_cops("CoPS")
+
+# reference data - they have additional columns compared to `antibiotics` and `microorganisms` to improve speed
+AB_lookup <- create_AB_lookup()
+MO_lookup <- create_MO_lookup()
+MO.old_lookup <- create_MO.old_lookup()
+
+# antibiotic groups
+# (these will also be used for eucast_rules() and understanding data-raw/eucast_rules.tsv)
+globalenv_before_ab <- c(ls(envir = globalenv()), "globalenv_before_ab")
+AMINOGLYCOSIDES <- antibiotics %>% filter(group %like% "aminoglycoside") %>% pull(ab)
+AMINOPENICILLINS <- as.ab(c("AMP", "AMX"))
+CARBAPENEMS <- antibiotics %>% filter(group %like% "carbapenem") %>% pull(ab)
+CEPHALOSPORINS <- antibiotics %>% filter(group %like% "cephalosporin") %>% pull(ab)
+CEPHALOSPORINS_1ST <- antibiotics %>% filter(group %like% "cephalosporin.*1") %>% pull(ab)
+CEPHALOSPORINS_2ND <- antibiotics %>% filter(group %like% "cephalosporin.*2") %>% pull(ab)
+CEPHALOSPORINS_3RD <- antibiotics %>% filter(group %like% "cephalosporin.*3") %>% pull(ab)
+CEPHALOSPORINS_EXCEPT_CAZ <- CEPHALOSPORINS[CEPHALOSPORINS != "CAZ"]
+FLUOROQUINOLONES <- antibiotics %>% filter(atc_group2 %like% "fluoroquinolone") %>% pull(ab)
+LIPOGLYCOPEPTIDES <- as.ab(c("DAL", "ORI", "TLV")) # dalba/orita/tela
+GLYCOPEPTIDES <- antibiotics %>% filter(group %like% "glycopeptide") %>% pull(ab)
+GLYCOPEPTIDES_EXCEPT_LIPO <- GLYCOPEPTIDES[!GLYCOPEPTIDES %in% LIPOGLYCOPEPTIDES]
+LINCOSAMIDES <- antibiotics %>% filter(atc_group2 %like% "lincosamide") %>% pull(ab) %>% c("PRL")
+MACROLIDES <- antibiotics %>% filter(atc_group2 %like% "macrolide") %>% pull(ab)
+OXAZOLIDINONES <- antibiotics %>% filter(group %like% "oxazolidinone") %>% pull(ab)
+PENICILLINS <- antibiotics %>% filter(group %like% "penicillin") %>% pull(ab)
+POLYMYXINS <- antibiotics %>% filter(group %like% "polymyxin") %>% pull(ab)
+STREPTOGRAMINS <- antibiotics %>% filter(atc_group2 %like% "streptogramin") %>% pull(ab)
+TETRACYCLINES <- antibiotics %>% filter(atc_group2 %like% "tetracycline") %>% pull(ab)
+TETRACYCLINES_EXCEPT_TGC <- TETRACYCLINES[TETRACYCLINES != "TGC"]
+UREIDOPENICILLINS <- as.ab(c("PIP", "TZP", "AZL", "MEZ"))
+BETALACTAMS <- c(PENICILLINS, CEPHALOSPORINS, CARBAPENEMS)
+
+DEFINED_AB_GROUPS <- ls(envir = globalenv())
+DEFINED_AB_GROUPS <- DEFINED_AB_GROUPS[!DEFINED_AB_GROUPS %in% globalenv_before_ab]
+
+# Export to package as internal data ----
+usethis::use_data(eucast_rules_file, 
+                  translations_file,
+                  microorganisms.translation,
+                  INTRINSIC_R,
+                  LANGUAGES_SUPPORTED,
+                  MO_CONS,
+                  MO_COPS,
+                  AB_lookup,
+                  MO_lookup,
+                  MO.old_lookup,
+                  AMINOGLYCOSIDES,
+                  AMINOPENICILLINS,
+                  CARBAPENEMS,
+                  CEPHALOSPORINS,
+                  CEPHALOSPORINS_1ST,
+                  CEPHALOSPORINS_2ND,
+                  CEPHALOSPORINS_3RD,
+                  CEPHALOSPORINS_EXCEPT_CAZ,
+                  FLUOROQUINOLONES,
+                  LIPOGLYCOPEPTIDES,
+                  GLYCOPEPTIDES,
+                  GLYCOPEPTIDES_EXCEPT_LIPO,
+                  LINCOSAMIDES,
+                  MACROLIDES,
+                  OXAZOLIDINONES,
+                  PENICILLINS,
+                  POLYMYXINS,
+                  STREPTOGRAMINS,
+                  TETRACYCLINES,
+                  TETRACYCLINES_EXCEPT_TGC,
+                  UREIDOPENICILLINS,
+                  BETALACTAMS,
+                  DEFINED_AB_GROUPS,
+                  internal = TRUE,
+                  overwrite = TRUE,
+                  version = 2,
+                  compress = "xz")
+
+# Export data sets to the repository in different formats -----------------
+
+write_md5 <- function(object) {
+  conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
+  writeLines(digest::digest(object, "md5"), conn)
+  close(conn)
+}
+changed_md5 <- function(object) {
+  tryCatch({
+    conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
+    compared <- digest::digest(object, "md5") != readLines(con = conn)
+    close(conn)
+    compared
+  }, error = function(e) TRUE)
+}
+
+# give official names to ABs and MOs
+rsi <- dplyr::mutate(rsi_translation, ab = ab_name(ab), mo = mo_name(mo))
+if (changed_md5(rsi)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('rsi_translation')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(rsi)
+  try(saveRDS(rsi, "data-raw/rsi_translation.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(rsi, "data-raw/rsi_translation.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(rsi, "data-raw/rsi_translation.sas"), silent = TRUE)
+  try(haven::write_sav(rsi, "data-raw/rsi_translation.sav"), silent = TRUE)
+  try(haven::write_dta(rsi, "data-raw/rsi_translation.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(rsi, "data-raw/rsi_translation.xlsx"), silent = TRUE)
+}
+
+mo <- dplyr::mutate_if(microorganisms, ~!is.numeric(.), as.character)
+if (changed_md5(mo)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('microorganisms')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(mo)
+  try(saveRDS(mo, "data-raw/microorganisms.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(mo, "data-raw/microorganisms.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(dplyr::select(mo, -snomed), "data-raw/microorganisms.sas"), silent = TRUE)
+  try(haven::write_sav(dplyr::select(mo, -snomed), "data-raw/microorganisms.sav"), silent = TRUE)
+  try(haven::write_dta(dplyr::select(mo, -snomed), "data-raw/microorganisms.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(mo, "data-raw/microorganisms.xlsx"), silent = TRUE)
+}
+
+if (changed_md5(microorganisms.old)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('microorganisms.old')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(microorganisms.old)
+  try(saveRDS(microorganisms.old, "data-raw/microorganisms.old.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(microorganisms.old, "data-raw/microorganisms.old.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(microorganisms.old, "data-raw/microorganisms.old.sas"), silent = TRUE)
+  try(haven::write_sav(microorganisms.old, "data-raw/microorganisms.old.sav"), silent = TRUE)
+  try(haven::write_dta(microorganisms.old, "data-raw/microorganisms.old.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(microorganisms.old, "data-raw/microorganisms.old.xlsx"), silent = TRUE)
+}
+
+ab <- dplyr::mutate_if(antibiotics, ~!is.numeric(.), as.character)
+if (changed_md5(ab)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('antibiotics')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(ab)
+  try(saveRDS(ab, "data-raw/antibiotics.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(ab, "data-raw/antibiotics.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(ab, "data-raw/antibiotics.sas"), silent = TRUE)
+  try(haven::write_sav(ab, "data-raw/antibiotics.sav"), silent = TRUE)
+  try(haven::write_dta(ab, "data-raw/antibiotics.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(ab, "data-raw/antibiotics.xlsx"), silent = TRUE)
+}
+
+av <- dplyr::mutate_if(antivirals, ~!is.numeric(.), as.character)
+if (changed_md5(av)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('antivirals')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(av)
+  try(saveRDS(av, "data-raw/antivirals.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(av, "data-raw/antivirals.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(av, "data-raw/antivirals.sas"), silent = TRUE)
+  try(haven::write_sav(av, "data-raw/antivirals.sav"), silent = TRUE)
+  try(haven::write_dta(av, "data-raw/antivirals.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(av, "data-raw/antivirals.xlsx"), silent = TRUE)
+}
+
+if (changed_md5(intrinsic_resistant)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('intrinsic_resistant')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(intrinsic_resistant)
+  try(saveRDS(intrinsic_resistant, "data-raw/intrinsic_resistant.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(intrinsic_resistant, "data-raw/intrinsic_resistant.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(intrinsic_resistant, "data-raw/intrinsic_resistant.sas"), silent = TRUE)
+  try(haven::write_sav(intrinsic_resistant, "data-raw/intrinsic_resistant.sav"), silent = TRUE)
+  try(haven::write_dta(intrinsic_resistant, "data-raw/intrinsic_resistant.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(intrinsic_resistant, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
+}
+
+if (changed_md5(dosage)) {
+  usethis::ui_info(paste0("Saving {usethis::ui_value('dosage')} to {usethis::ui_value('/data-raw/')}"))
+  write_md5(dosage)
+  try(saveRDS(dosage, "data-raw/dosage.rds", version = 2, compress = "xz"), silent = TRUE)
+  try(write.table(dosage, "data-raw/dosage.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
+  try(haven::write_sas(dosage, "data-raw/dosage.sas"), silent = TRUE)
+  try(haven::write_sav(dosage, "data-raw/dosage.sav"), silent = TRUE)
+  try(haven::write_dta(dosage, "data-raw/dosage.dta"), silent = TRUE)
+  try(openxlsx::write.xlsx(dosage, "data-raw/dosage.xlsx"), silent = TRUE)
+}
+
+# remove leftovers from global env
+current_globalenv <- ls(envir = globalenv())
+rm(list = current_globalenv[!current_globalenv %in% old_globalenv])
+rm(current_globalenv)
--- a/data-raw/ab.md5
+++ b/data-raw/ab.md5
@@ -1 +1 @@
-a30faa0e4475d440d1bb8e44e6857062
+f6b4a2325c4bf3a8d4da337c8cfa07be
--- a/data-raw/antibiotics.dta
+++ b/data-raw/antibiotics.dta
--- a/data-raw/antibiotics.rds
+++ b/data-raw/antibiotics.rds
--- a/data-raw/antibiotics.sas
+++ b/data-raw/antibiotics.sas
--- a/data-raw/antibiotics.sav
+++ b/data-raw/antibiotics.sav
--- a/data-raw/antibiotics.txt
+++ b/data-raw/antibiotics.txt
@@ -1,6 +1,6 @@
 "ab"	"atc"	"cid"	"name"	"group"	"atc_group1"	"atc_group2"	"abbreviations"	"synonyms"	"oral_ddd"	"oral_units"	"iv_ddd"	"iv_units"	"loinc"
 "AMA"	"J04AA01"	4649	"4-aminosalicylic acid"	"Antimycobacterials"	"Drugs for treatment of tuberculosis"	"Aminosalicylic acid and derivatives"	""	"c(\"aminopar\", \"aminosalicylic\", \"aminosalicylic acid\", \"aminosalyl\", \"aminox\", \"apacil\", \"deapasil\", \"entepas\", \"ferrosan\", \"gabbropas\", \"helipidyl\", \"hellipidyl\", \"neopasalate\", \"osacyl\", \"pamacyl\", \"pamisyl\", \"paramycin\", \"parasal\", \"parasalicil\", \"parasalindon\", \"pasalon\", \"pasara\", \"pascorbic\", \"pasdium\", \"paser granules\", \"paskalium\", \"pasmed\", \"pasnodia\", \"pasolac\", \"propasa\", \"rezipas\", \"teebacin\", \"wln: zr cq dvq\")"	12	"g"			"character(0)"
-"FCT"	"D01AE21"	3366	"5-fluorocytosine"	"Antifungals/antimycotics"	"Antifungals for topical use"	"Other antifungals for topical use"	"c(\"5flc\", \"fluo\")"	"c(\"alcobon\", \"ancobon\", \"ancotil\", \"ancotyl\", \"flucitosina\", \"flucystine\", \"flucytosin\", \"flucytosine\", \"flucytosinum\", \"flucytosone\", \"fluocytosine\", \"fluorcytosine\")"					"c(\"10974-4\", \"23805-5\", \"25142-1\", \"25143-9\", \"3639-2\", \"46218-4\")"
+"FCT"	"D01AE21"	3366	"5-fluorocytosine"	"Antifungals/antimycotics"	"Antifungals for topical use"	"Other antifungals for topical use"	"c(\"5flc\", \"fcu\", \"fluo\", \"fluy\")"	"c(\"alcobon\", \"ancobon\", \"ancotil\", \"ancotyl\", \"flucitosina\", \"flucystine\", \"flucytosin\", \"flucytosine\", \"flucytosinum\", \"flucytosone\", \"fluocytosine\", \"fluorcytosine\")"					"c(\"10974-4\", \"23805-5\", \"25142-1\", \"25143-9\", \"3639-2\", \"46218-4\")"
 "ACM"		6450012	"Acetylmidecamycin"	"Macrolides/lincosamides"			""	""					""
 "ASP"		49787020	"Acetylspiramycin"	"Macrolides/lincosamides"			""	"c(\"acetylspiramycin\", \"foromacidin b\", \"spiramycin ii\")"					"character(0)"
 "ALS"	"J04BA03"	8954	"Aldesulfone sodium"	"Other antibacterials"	"Drugs for treatment of lepra"	"Drugs for treatment of lepra"	""	"c(\"adesulfone sodium\", \"aldapsone\", \"aldesulfona sodica\", \"aldesulfone\", \"aldesulfone sodique\", \"aldesulfone sodium\", \"diamidin\", \"diasone\", \"diasone sodium\", \"diazon\", \"novotrone\", \"sodium aldesulphone\", \"sodium sulfoxone\", \"sulfoxone sodium\")"	0.33	"g"			"character(0)"
@@ -10,7 +10,7 @@
 \"piramox\", \"robamox\", \"sawamox pm\", \"tolodina\", \"unicillin\", \"utimox\", \"vetramox\")"	1.5	"g"	3	"g"	"c(\"16365-9\", \"25274-2\", \"3344-9\", \"80133-2\")"
 "AMC"	"J01CR02"	23665637	"Amoxicillin/clavulanic acid"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Combinations of penicillins, incl. beta-lactamase inhibitors"	"c(\"a/c\", \"amcl\", \"aml\", \"aug\", \"xl\")"	"c(\"amocla\", \"amoclan\", \"amoclav\", \"amoxsiklav\", \"augmentan\", \"augmentin\", \"augmentin xr\", \"augmentine\", \"auspilic\", \"clamentin\", \"clamobit\", \"clavamox\", \"clavinex\", \"clavoxilin plus\", \"clavulin\", \"clavumox\", \"coamoxiclav\", \"eumetinex\", \"kmoxilin\", \"spectramox\", \"spektramox\", \"viaclav\", \"xiclav\")"	1.5	"g"	3	"g"	"character(0)"
 "AXS"		465441	"Amoxicillin/sulbactam"	"Beta-lactams/penicillins"			""	""					""
-"AMB"	"J02AA01"	5280965	"Amphotericin B"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Antibiotics"	"c(\"amfb\", \"amph\")"	"c(\"abelcet\", \"abelecet\", \"ambisome\", \"amfotericina b\", \"amphocin\", \"amphomoronal\", \"amphortericin b\", \"amphotec\", \"amphotericin\", \"amphotericin b\", \"amphotericine b\", \"amphotericinum b\", \"amphozone\", \"anfotericine b\", \"fungilin\", \"fungisome\", \"fungisone\", \"fungizone\", \"halizon\")"			35	"mg"	"c(\"16370-9\", \"3353-0\", \"3354-8\", \"40707-2\", \"40757-7\", \"49859-2\")"
+"AMB"	"J02AA01"	5280965	"Amphotericin B"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Antibiotics"	"c(\"amf\", \"amfb\", \"amph\")"	"c(\"abelcet\", \"abelecet\", \"ambisome\", \"amfotericina b\", \"amphocin\", \"amphomoronal\", \"amphortericin b\", \"amphotec\", \"amphotericin\", \"amphotericin b\", \"amphotericine b\", \"amphotericinum b\", \"amphozone\", \"anfotericine b\", \"fungilin\", \"fungisome\", \"fungisone\", \"fungizone\", \"halizon\")"			35	"mg"	"c(\"16370-9\", \"3353-0\", \"3354-8\", \"40707-2\", \"40757-7\", \"49859-2\")"
 "AMH"			"Amphotericin B-high"	"Aminoglycosides"			"c(\"amfo b high\", \"amhl\", \"ampho b high\", \"amphotericin high\")"	""					""
 "AMP"	"J01CA01"	6249	"Ampicillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	"c(\"am\", \"amp\", \"ampi\")"	"c(\"acillin\", \"adobacillin\", \"amblosin\", \"amcill\", \"amfipen\", \"amfipen v\", \"amipenix s\", \"ampichel\", \"ampicil\", \"ampicilina\", \"ampicillin\", \"ampicillin a\", \"ampicillin acid\", \"ampicillin anhydrate\", \"ampicillin anhydrous\", \"ampicillin base\", \"ampicillin sodium\", \"ampicillina\", \"ampicilline\", \"ampicillinum\", \"ampicin\", \"ampifarm\", \"ampikel\", \"ampimed\", \"ampipenin\", \"ampiscel\", \"ampisyn\", \"ampivax\", \"ampivet\", \"amplacilina\", \"amplin\", \"amplipenyl\", \"amplisom\", \"amplital\", \"anhydrous ampicillin\", \"austrapen\", 
 \"binotal\", \"bonapicillin\", \"britacil\", \"campicillin\", \"copharcilin\", \"delcillin\", \"deripen\", \"divercillin\", \"doktacillin\", \"duphacillin\", \"grampenil\", \"guicitrina\", \"guicitrine\", \"lifeampil\", \"marcillin\", \"morepen\", \"norobrittin\", \"nuvapen\", \"olin kid\", \"omnipen\", \"orbicilina\", \"pen a oral\", \"pen ampil\", \"penbristol\", \"penbritin\", \"penbritin paediatric\", \"penbritin syrup\", \"penbrock\", \"penicline\", \"penimic\", \"pensyn\", \"pentrex\", \"pentrexl\", \"pentrexyl\", \"pentritin\", \"pfizerpen a\", \"polycillin\", \"polyflex\", 
@@ -37,6 +37,8 @@
 "BEK"		439318	"Bekanamycin"	"Aminoglycosides"			""	"c(\"aminodeoxykanamycin\", \"becanamicina\", \"bekanamycin\", \"bekanamycine\", \"bekanamycinum\", \"nebramycin v\")"					"character(0)"
 "BNB"	"J01CE08"		"Benzathine benzylpenicillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase sensitive penicillins"	""	""			3.6	"g"	""
 "BNP"	"J01CE10"	64725	"Benzathine phenoxymethylpenicillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase sensitive penicillins"	""	"c(\"bicillin v\", \"biphecillin\")"	2	"g"			"character(0)"
+"PEN"	"J01CE01"	5904	"Benzylpenicillin"	"Beta-lactams/penicillins"	"Combinations of antibacterials"	"Combinations of antibacterials"	"c(\"bepe\", \"pen\", \"peni\", \"peni g\", \"penicillin\", \"penicillin g\", \"pg\")"	"c(\"abbocillin\", \"ayercillin\", \"bencilpenicilina\", \"benzopenicillin\", \"benzyl penicillin\", \"benzylpenicillin\", \"benzylpenicillin g\", \"benzylpenicilline\", \"benzylpenicillinum\", \"bicillin\", \"cillora\", \"cilloral\", \"cilopen\", \"compocillin g\", \"cosmopen\", \"dropcillin\", \"free penicillin g\", \"free penicillin ii\", \"galofak\", \"gelacillin\", \"liquacillin\", \"megacillin\", \"pencillin g\", \"penicillin\", \"penicilling\", \"pentids\", \"permapen\", \"pfizerpen\", \"pfizerpen g\", \"pharmacillin\", \"pradupen\", \"specilline g\", \"ursopen\"
+)"			3.6	"g"	"3913-1"
 "BES"		10178705	"Besifloxacin"	"Quinolones"			""	"besifloxacin"					"character(0)"
 "BIA"		71339	"Biapenem"	"Carbapenems"			""	"c(\"biapenem\", \"biapenern\", \"bipenem\", \"omegacin\")"					"character(0)"
 "BCZ"		65807	"Bicyclomycin (Bicozamycin)"	"Other antibacterials"			""	"c(\"aizumycin\", \"bacfeed\", \"bacteron\", \"bicozamicina\", \"bicozamycin\", \"bicozamycine\", \"bicozamycinum\")"					"character(0)"
@@ -106,7 +108,7 @@
 "CPT"	"J01DI02"	56841980	"Ceftaroline"	"Cephalosporins (5th gen.)"			"c(\"\", \"cfro\")"	"c(\"teflaro\", \"zinforo\")"			1.2		"character(0)"
 "CPA"			"Ceftaroline/avibactam"	"Cephalosporins (5th gen.)"			""	""					""
 "CAZ"	"J01DD02"	5481173	"Ceftazidime"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"caz\", \"cefta\", \"cfta\", \"cftz\", \"taz\", \"tz\", \"xtz\")"	"c(\"ceftazidim\", \"ceftazidima\", \"ceftazidime\", \"ceftazidimum\", \"ceptaz\", \"fortaz\", \"fortum\", \"pentacef\", \"tazicef\", \"tazidime\")"			4	"g"	"c(\"21151-6\", \"3449-6\", \"80960-8\")"
-"CZA"			"Ceftazidime/avibactam"	"Cephalosporins (3rd gen.)"			"c(\"\", \"cfav\")"	""					""
+"CZA"	"J01DD52"	90643431	"Ceftazidime/avibactam"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"\", \"cfav\")"	"c(\"avycaz\", \"zavicefta\")"			6	"g"	""
 "CCV"	"J01DD52"	9575352	"Ceftazidime/clavulanic acid"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"czcl\", \"xtzl\")"	""			6		""
 "CEM"		6537431	"Cefteram"	"Cephalosporins (3rd gen.)"			""	"c(\"cefteram\", \"cefterame\", \"cefteramum\", \"ceftetrame\")"					"character(0)"
 "CPL"		5362114	"Cefteram pivoxil"	"Cephalosporins (3rd gen.)"			""	"c(\"cefteram pivoxil\", \"tomiron\")"					"character(0)"
@@ -154,7 +156,7 @@
 "CTR"	"G01AF02"	2812	"Clotrimazole"	"Antifungals/antimycotics"			"clot"	"c(\"canesten\", \"canesten cream\", \"canesten solution\", \"canestene\", \"canestine\", \"canifug\", \"chlotrimazole\", \"cimitidine\", \"clomatin\", \"clotrimaderm\", \"clotrimaderm cream\", \"clotrimazol\", \"clotrimazole\", \"clotrimazolum\", \"cutistad\", \"desamix f\", \"diphenylmethane\", \"empecid\", \"esparol\", \"fem care\", \"femcare\", \"gyne lotrimin\", \"jidesheng\", \"kanesten\", \"klotrimazole\", \"lotrimax\", \"lotrimin\", \"lotrimin af\", \"lotrimin af cream\", \"lotrimin af lotion\", \"lotrimin af solution\", \"lotrimin cream\", \"lotrimin lotion\", 
 \"lotrimin solution\", \"monobaycuten\", \"mycelax\", \"mycelex\", \"mycelex cream\", \"mycelex g\", \"mycelex otc\", \"mycelex solution\", \"mycelex troches\", \"mycelex twin pack\", \"myclo cream\", \"myclo solution\", \"myclo spray solution\", \"mycofug\", \"mycosporin\", \"mykosporin\", \"nalbix\", \"otomax\", \"pedisafe\", \"rimazole\", \"stiemazol\", \"tibatin\", \"trimysten\", \"veltrim\")"					"character(0)"
 "CLO"	"J01CF02"	6098	"Cloxacillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase resistant penicillins"	"c(\"\", \"clox\")"	"c(\"chloroxacillin\", \"clossacillina\", \"cloxacilina\", \"cloxacillin\", \"cloxacillin sodium\", \"cloxacilline\", \"cloxacillinna\", \"cloxacillinum\", \"cloxapen\", \"methocillin s\", \"orbenin\", \"syntarpen\", \"tegopen\")"	2	"g"	2	"g"	"c(\"16628-0\", \"25250-2\")"
-"COL"	"J01XB01"	5311054	"Colistin"	"Polymyxins"	"Other antibacterials"	"Polymyxins"	"c(\"cl\", \"coli\", \"cs\", \"ct\")"	"c(\"belcomycine\", \"colimycin\", \"colimycin sulphate\", \"colisticin\", \"colistimethate\", \"colistimethate sodium\", \"colistin sulfate\", \"colistin sulphate\", \"colomycin\", \"coly-mycin\", \"polymyxin e\", \"polymyxin e. sulfate\", \"promixin\", \"totazina\")"			9	"MU"	"c(\"16645-4\", \"29493-4\")"
+"COL"	"J01XB01"	5311054	"Colistin"	"Polymyxins"	"Other antibacterials"	"Polymyxins"	"c(\"cl\", \"coli\", \"cs\", \"cst\", \"ct\")"	"c(\"belcomycine\", \"colimycin\", \"colimycin sulphate\", \"colisticin\", \"colistimethate\", \"colistimethate sodium\", \"colistin sulfate\", \"colistin sulphate\", \"colomycin\", \"coly-mycin\", \"polymyxin e\", \"polymyxin e. sulfate\", \"promixin\", \"totazina\")"			9	"MU"	"c(\"16645-4\", \"29493-4\")"
 "COP"			"Colistin/polysorbate"	"Other antibacterials"			""	""					""
 "CYC"	"J04AB01"	6234	"Cycloserine"	"Oxazolidinones"	"Drugs for treatment of tuberculosis"	"Antibiotics"	"cycl"	"c(\"cicloserina\", \"closerin\", \"closina\", \"cyclorin\", \"cycloserin\", \"cycloserine\", \"cycloserinum\", \"farmiserina\", \"micoserina\", \"miroserina\", \"miroseryn\", \"novoserin\", \"oxamicina\", \"oxamycin\", \"seromycin\", \"tebemicina\", \"tisomycin\", \"wasserina\")"	0.75	"g"			"c(\"16702-3\", \"25251-0\", \"3519-6\")"
 "DAL"	"J01XA04"	23724878	"Dalbavancin"	"Glycopeptides"	"Other antibacterials"	"Glycopeptide antibacterials"	"dalb"	"c(\"dalbavancin\", \"dalvance\")"			1.5		"character(0)"
@@ -196,23 +198,23 @@
 "FLO"		65864	"Flomoxef"	"Other antibacterials"			""	"c(\"flomoxef\", \"flomoxefo\", \"flomoxefum\")"					"character(0)"
 "FLR"		114811	"Florfenicol"	"Other antibacterials"			""	"c(\"aquafen\", \"florfenicol\", \"nuflor\", \"nuflor gold\")"					"87599-7"
 "FLC"	"J01CF05"	21319	"Flucloxacillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase resistant penicillins"	"c(\"clox\", \"flux\")"	"c(\"floxacillin\", \"floxapen\", \"floxapen sodium salt\", \"fluclox\", \"flucloxacilina\", \"flucloxacillin\", \"flucloxacilline\", \"flucloxacillinum\", \"fluorochloroxacillin\")"	2	"g"	2	"g"	"character(0)"
-"FLU"	"J02AC01"	3365	"Fluconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"c(\"fluc\", \"fluz\")"	"c(\"alflucoz\", \"alfumet\", \"biocanol\", \"biozole\", \"biozolene\", \"canzol\", \"cryptal\", \"diflazon\", \"diflucan\", \"dimycon\", \"elazor\", \"flucazol\", \"fluconazol\", \"fluconazole\", \"fluconazole capsules\", \"fluconazolum\", \"flucostat\", \"flukezol\", \"flunazol\", \"flunizol\", \"flusol\", \"fluzon\", \"fluzone\", \"forcan\", \"fuconal\", \"fungata\", \"loitin\", \"oxifugol\", \"pritenzol\", \"syscan\", \"trican\", \"triconal\", \"triflucan\", \"zoltec\")"	0.2	"g"	0.2	"g"	"c(\"10987-6\", \"16870-8\", \"25255-1\", \"80530-9\")"
+"FLU"	"J02AC01"	3365	"Fluconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"c(\"fluc\", \"fluz\", \"flz\")"	"c(\"alflucoz\", \"alfumet\", \"biocanol\", \"biozole\", \"biozolene\", \"canzol\", \"cryptal\", \"diflazon\", \"diflucan\", \"dimycon\", \"elazor\", \"flucazol\", \"fluconazol\", \"fluconazole\", \"fluconazole capsules\", \"fluconazolum\", \"flucostat\", \"flukezol\", \"flunazol\", \"flunizol\", \"flusol\", \"fluzon\", \"fluzone\", \"forcan\", \"fuconal\", \"fungata\", \"loitin\", \"oxifugol\", \"pritenzol\", \"syscan\", \"trican\", \"triconal\", \"triflucan\", \"zoltec\")"	0.2	"g"	0.2	"g"	"c(\"10987-6\", \"16870-8\", \"25255-1\", \"80530-9\")"
 "FLM"	"J01MB07"	3374	"Flumequine"	"Quinolones"	"Quinolone antibacterials"	"Other quinolones"	""	"c(\"apurone\", \"fantacin\", \"flumequine\", \"flumequino\", \"flumequinum\", \"flumigal\", \"flumiquil\", \"flumisol\", \"flumix\", \"imequyl\")"	1.2	"g"			"character(0)"
 "FLR1"	"J01FA14"	71260	"Flurithromycin"	"Macrolides/lincosamides"	"Macrolides, lincosamides and streptogramins"	"Macrolides"	""	"c(\"flurithromicina\", \"flurithromycime\", \"flurithromycin\", \"flurithromycine\", \"flurithromycinum\", \"fluritromicina\", \"fluritromycinum\", \"flurizic\")"	0.75	"g"			"character(0)"
 "FFL"		214356	"Fosfluconazole"	"Antifungals/antimycotics"			""	"c(\"fosfluconazole\", \"phosfluconazole\", \"procif\", \"prodif\")"					"character(0)"
-"FOS"	"J01XX01"	446987	"Fosfomycin"	"Other antibacterials"	"Other antibacterials"	"Other antibacterials"	"c(\"ff\", \"fm\", \"fo\", \"fos\", \"fosf\")"	"c(\"fosfocina\", \"fosfomicina\", \"fosfomycin\", \"fosfomycin sodium\", \"fosfomycine\", \"fosfomycinum\", \"fosfonomycin\", \"monuril\", \"monurol\", \"phosphonemycin\", \"phosphonomycin\", \"veramina\")"	3	"g"	8	"g"	"character(0)"
+"FOS"	"J01XX01"	446987	"Fosfomycin"	"Other antibacterials"	"Other antibacterials"	"Other antibacterials"	"c(\"ff\", \"fm\", \"fo\", \"fof\", \"fos\", \"fosf\")"	"c(\"fosfocina\", \"fosfomicina\", \"fosfomycin\", \"fosfomycin sodium\", \"fosfomycine\", \"fosfomycinum\", \"fosfonomycin\", \"monuril\", \"monurol\", \"phosphonemycin\", \"phosphonomycin\", \"veramina\")"	3	"g"	8	"g"	"character(0)"
 "FMD"		572	"Fosmidomycin"	"Other antibacterials"			""	"c(\"fosmidomycin\", \"fosmidomycina\", \"fosmidomycine\", \"fosmidomycinum\")"					"character(0)"
 "FRM"		8378	"Framycetin"	"Aminoglycosides"			"c(\"\", \"fram\")"	"c(\"actilin\", \"actiline\", \"antibiotique\", \"bycomycin\", \"endomixin\", \"enterfram\", \"fradiomycin\", \"fradiomycin b\", \"fradiomycinum\", \"framicetina\", \"framycetin\", \"framycetin sulfate\", \"framycetine\", \"framycetinum\", \"framygen\", \"fraquinol\", \"jernadex\", \"myacine\", \"myacyne\", \"mycerin\", \"mycifradin\", \"neobrettin\", \"neolate\", \"neomas\", \"neomcin\", \"neomicina\", \"neomin\", \"neomycin\", \"neomycin b\", \"neomycin b sulfate\", \"neomycin solution\", \"neomycin sulfate\", \"neomycin sulphate\", \"neomycinb\", \"neomycine\", \"neomycinum\", 
 \"nivemycin\", \"pimavecort\", \"soframycin\", \"soframycine\", \"tuttomycin\", \"vonamycin\", \"vonamycin powder v\")"					"character(0)"
 "FRZ"		5323714	"Furazolidone"	"Other antibacterials"			""	"c(\"bifuron\", \"corizium\", \"coryzium\", \"diafuron\", \"enterotoxon\", \"furall\", \"furaxon\", \"furaxone\", \"furazol\", \"furazolidine\", \"furazolidon\", \"furazolidona\", \"furazolidone\", \"furazolidonum\", \"furazolum\", \"furazon\", \"furidon\", \"furovag\", \"furox aerosol powder\", \"furoxal\", \"furoxane\", \"furoxon\", \"furoxone\", \"furoxone liquid\", \"furoxone swine mix\", \"furozolidine\", \"giardil\", \"giarlam\", \"medaron\", \"neftin\", \"nicolen\", \"nifulidone\", \"nifuran\", \"nifurazolidone\", \"nifurazolidonum\", \"nitrofurazolidone\", \"nitrofurazolidonum\", 
 \"nitrofuroxon\", \"optazol\", \"ortazol\", \"puradin\", \"roptazol\", \"sclaventerol\", \"tikofuran\", \"topazone\", \"trichofuron\", \"tricofuron\", \"tricoron\", \"trifurox\", \"viofuragyn\")"					"character(0)"
-"FUS"	"J01XC01"	3000226	"Fusidic acid"	"Other antibacterials"	"Other antibacterials"	"Steroid antibacterials"	"fusi"	"c(\"acide fusidique\", \"acido fusidico\", \"acidum fusidicum\", \"flucidin\", \"fucidate\", \"fucidate sodium\", \"fucidic acid\", \"fucidin\", \"fucidin acid\", \"fucithalmic\", \"fusidate\", \"fusidate acid\", \"fusidic acid\", \"fusidine\", \"fusidinic acid\", \"ramycin\")"	1.5	"g"	1.5	"g"	"character(0)"
+"FUS"	"J01XC01"	3000226	"Fusidic acid"	"Other antibacterials"	"Other antibacterials"	"Steroid antibacterials"	"c(\"fa\", \"fusi\")"	"c(\"acide fusidique\", \"acido fusidico\", \"acidum fusidicum\", \"flucidin\", \"fucidate\", \"fucidate sodium\", \"fucidic acid\", \"fucidin\", \"fucidin acid\", \"fucithalmic\", \"fusidate\", \"fusidate acid\", \"fusidic acid\", \"fusidine\", \"fusidinic acid\", \"ramycin\")"	1.5	"g"	1.5	"g"	"character(0)"
 "GAM"		59364992	"Gamithromycin"	"Macrolides/lincosamides"			""	"gamithromycin"					"character(0)"
 "GRN"		124093	"Garenoxacin"	"Quinolones"			""	"c(\"ganefloxacin\", \"garenfloxacin\", \"garenoxacin\")"					"character(0)"
 "GAT"	"J01MA16"	5379	"Gatifloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	"c(\"\", \"gati\")"	"c(\"gatiflo\", \"gatifloxacin\", \"gatifloxacine\", \"gatifloxcin\", \"gatilox\", \"gatiquin\", \"gatispan\", \"tequin\", \"tequin and zymar\", \"zymaxid\")"	0.4	"g"	0.4	"g"	"character(0)"
 "GEM"	"J01MA15"	9571107	"Gemifloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	""	"c(\"factiv\", \"factive\", \"gemifioxacin\", \"gemifloxacin\", \"gemifloxacine\", \"gemifloxacino\", \"gemifloxacinum\")"	0.32				"character(0)"
 "GEN"	"J01GB03"	3467	"Gentamicin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Other aminoglycosides"	"c(\"cn\", \"gen\", \"gent\", \"gm\")"	"c(\"apogen\", \"centicin\", \"cidomycin\", \"garasol\", \"genoptic liquifilm\", \"genoptic s.o.p.\", \"gentacycol\", \"gentafair\", \"gentak\", \"gentamar\", \"gentamcin sulfate\", \"gentamicin\", \"gentamicina\", \"gentamicine\", \"gentamicins\", \"gentamicinum\", \"gentamycin\", \"gentamycins\", \"gentamycinum\", \"gentavet\", \"gentocin\", \"jenamicin\", \"lyramycin\", \"oksitselanim\", \"refobacin\", \"refobacin tm\", \"septigen\", \"uromycine\")"			0.24	"g"	"c(\"13561-6\", \"13562-4\", \"15106-8\", \"22746-2\", \"22747-0\", \"31091-2\", \"31092-0\", \"31093-8\", \"35668-3\", \"3663-2\", \"3664-0\", \"3665-7\", \"39082-3\", \"47109-4\", \"59379-8\", \"80971-5\", \"88111-0\")"
-"GEH"			"Gentamicin-high"	"Aminoglycosides"			"c(\"gehl\", \"genta high\", \"gentamicin high\")"	""					""
+"GEH"			"Gentamicin-high"	"Aminoglycosides"			"c(\"g_h\", \"gehl\", \"genta high\", \"gentamicin high\")"	""					""
 "GEP"		25101874	"Gepotidacin"	"Other antibacterials"			""	"gepotidacin"					"character(0)"
 "GRX"	"J01MA11"	72474	"Grepafloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	"c(\"\", \"grep\")"	"grepafloxacin"	0.4	"g"			"character(0)"
 "GRI"		441140	"Griseofulvin"	"Antifungals/antimycotics"			""	"c(\"amudane\", \"curling factor\", \"delmofulvina\", \"fulcin\", \"fulcine\", \"fulvican grisactin\", \"fulvicin\", \"fulvicin bolus\", \"fulvidex\", \"fulvina\", \"fulvinil\", \"fulvistatin\", \"fungivin\", \"greosin\", \"gresfeed\", \"gricin\", \"grifulin\", \"grifulvin\", \"grifulvin v\", \"grisactin\", \"grisactin ultra\", \"grisactin v\", \"griscofulvin\", \"grise ostatin\", \"grisefuline\", \"griseo\", \"griseofulvin\", \"griseofulvin forte\", \"griseofulvina\", \"griseofulvine\", \"griseofulvinum\", \"griseomix\", \"griseostatin\", \"grisetin\", \"grisofulvin\", 
@@ -236,9 +238,9 @@
 "ITR"	"J02AC02"	3793	"Itraconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"itra"	"c(\"itraconazol\", \"itraconazole\", \"itraconazolum\", \"itraconzaole\", \"itrazole\", \"oriconazole\", \"sporanox\")"	0.2	"g"	0.2	"g"	"c(\"10989-2\", \"12392-7\", \"25258-5\", \"27081-9\", \"32184-4\", \"32185-1\", \"80531-7\")"
 "JOS"	"J01FA07"	5282165	"Josamycin"	"Macrolides/lincosamides"	"Macrolides, lincosamides and streptogramins"	"Macrolides"	""	"c(\"josacine\", \"josamicina\", \"josamycin\", \"josamycine\", \"josamycinum\")"	2	"g"			"character(0)"
 "KAN"	"J01GB04"	6032	"Kanamycin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Other aminoglycosides"	"c(\"hlk\", \"k\", \"kan\", \"kana\", \"km\")"	"c(\"kanamicina\", \"kanamycin\", \"kanamycin a\", \"kanamycin base\", \"kanamycine\", \"kanamycinum\", \"kantrex\", \"kenamycin a\", \"klebcil\", \"liposomal kanamycin\")"			1	"g"	"c(\"23889-9\", \"3698-8\", \"3699-6\", \"3700-2\", \"47395-9\")"
-"KAH"			"Kanamycin-high"	"Aminoglycosides"			"c(\"\", \"kahl\")"	""					""
+"KAH"			"Kanamycin-high"	"Aminoglycosides"			"c(\"\", \"k_h\", \"kahl\")"	""					""
 "KAC"			"Kanamycin/cephalexin"	"Aminoglycosides"			""	""					""
-"KET"	"J02AB02"	456201	"Ketoconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Imidazole derivatives"	"keto"	"c(\"extina\", \"fungarest\", \"fungoral\", \"ketocanazole\", \"ketoconazol\", \"ketoconazole\", \"ketoconazolum\", \"ketoderm\", \"nizoral\", \"xolegel\")"	0.2	"g"			"c(\"10990-0\", \"12393-5\", \"25259-3\", \"60091-6\", \"60092-4\")"
+"KET"	"J02AB02"	456201	"Ketoconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Imidazole derivatives"	"c(\"keto\", \"ktc\")"	"c(\"extina\", \"fungarest\", \"fungoral\", \"ketocanazole\", \"ketoconazol\", \"ketoconazole\", \"ketoconazolum\", \"ketoderm\", \"nizoral\", \"xolegel\")"	0.2	"g"			"c(\"10990-0\", \"12393-5\", \"25259-3\", \"60091-6\", \"60092-4\")"
 "KIT"			"Kitasamycin (Leucomycin)"	"Macrolides/lincosamides"			""	""					""
 "LAS"		5360807	"Lasalocid"	"Other antibacterials"			""	"c(\"avatec\", \"lasalocid\", \"lasalocid a\", \"lasalocide\", \"lasalocide a\", \"lasalocido\", \"lasalocidum\")"					"87598-9"
 "LTM"	"J01DD06"	47499	"Latamoxef"	"Cephalosporins (3rd gen.)"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"c(\"mox\", \"moxa\")"	"c(\"disodium moxalactam\", \"festamoxin\", \"lamoxactam\", \"latamoxef\", \"latamoxefum\", \"shiomarin\")"			4	"g"	"character(0)"
@@ -328,8 +330,6 @@
 "PAZ"	"J01MA18"	65957	"Pazufloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	""	"c(\"pazufloxacin\", \"pazufloxacine\", \"pazufloxacino\", \"pazufloxacinum\")"			1	"g"	"character(0)"
 "PEF"	"J01MA03"	51081	"Pefloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	"c(\"\", \"pefl\")"	"c(\"abactal\", \"labocton\", \"pefloxacin\", \"pefloxacine\", \"pefloxacino\", \"pefloxacinum\", \"perfloxacin\", \"silver pefloxacin\")"	0.8	"g"	0.8	"g"	"3906-5"
 "PNM"	"J01CE06"	10250769	"Penamecillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase sensitive penicillins"	""	"c(\"hydroxymethyl\", \"penamecilina\", \"penamecillin\", \"penamecillina\", \"penamecilline\", \"penamecillinum\")"	1.05	"g"			"character(0)"
-"PEN"	"J01CE01"	5904	"Benzylpenicillin"	"Beta-lactams/penicillins"	"Combinations of antibacterials"	"Combinations of antibacterials"	"c(\"bepe\", \"pen\", \"peni\", \"peni g\", \"penicillin\", \"penicillin g\", \"pg\")"	"c(\"abbocillin\", \"ayercillin\", \"bencilpenicilina\", \"benzopenicillin\", \"benzyl penicillin\", \"benzylpenicillin\", \"benzylpenicillin g\", \"benzylpenicilline\", \"benzylpenicillinum\", \"bicillin\", \"cillora\", \"cilloral\", \"cilopen\", \"compocillin g\", \"cosmopen\", \"dropcillin\", \"free penicillin g\", \"free penicillin ii\", \"galofak\", \"gelacillin\", \"liquacillin\", \"megacillin\", \"pencillin g\", \"penicillin\", \"penicilling\", \"pentids\", \"permapen\", \"pfizerpen\", \"pfizerpen g\", \"pharmacillin\", \"pradupen\", \"specilline g\", \"ursopen\"
-)"			3.6	"g"	"3913-1"
 "PNO"			"Penicillin/novobiocin"	"Beta-lactams/penicillins"			""	""					""
 "PSU"			"Penicillin/sulbactam"	"Beta-lactams/penicillins"			""	""					""
 "PNM1"	"J01AA10"	54686187	"Penimepicycline"	"Tetracyclines"	"Tetracyclines"	"Tetracyclines"	""	"c(\"duamine\", \"hydrocycline\", \"penetracyne\", \"penimepiciclina\", \"penimepicycline\", \"penimepicyclinum\")"					"character(0)"
@@ -342,7 +342,7 @@
 "PPA"	"J01MB04"	4831	"Pipemidic acid"	"Quinolones"	"Quinolone antibacterials"	"Other quinolones"	"c(\"pipz\", \"pizu\")"	"c(\"acide pipemidique\", \"acido pipemidico\", \"acidum pipemidicum\", \"deblaston\", \"dolcol\", \"pipedac\", \"pipemid\", \"pipemidic\", \"pipemidic acid\", \"pipemidicacid\", \"pipram\", \"uromidin\")"	0.8	"g"			"character(0)"
 "PIP"	"J01CA12"	43672	"Piperacillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	"c(\"pi\", \"pip\", \"pipc\", \"pipe\", \"pp\")"	"c(\"isipen\", \"pentcillin\", \"peperacillin\", \"peracin\", \"piperacilina\", \"piperacillin\", \"piperacillin na\", \"piperacillin sodium\", \"piperacilline\", \"piperacillinum\", \"pipercillin\", \"pipracil\", \"pipril\")"			14	"g"	"c(\"25268-4\", \"3972-7\")"
 "PIS"			"Piperacillin/sulbactam"	"Beta-lactams/penicillins"			""	""					""
-"TZP"	"J01CR05"	461573	"Piperacillin/tazobactam"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Combinations of penicillins, incl. beta-lactamase inhibitors"	"c(\"p/t\", \"piptaz\", \"piptazo\", \"pita\", \"pt\", \"ptc\", \"ptz\", \"tzp\")"	"c(\"\", \"tazocel\", \"tazocillin\", \"tazocin\", \"zosyn\")"			14	"g"	"character(0)"
+"TZP"	"J01CR05"	461573	"Piperacillin/tazobactam"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Combinations of penicillins, incl. beta-lactamase inhibitors"	"c(\"p/t\", \"piptaz\", \"piptazo\", \"pit\", \"pita\", \"pt\", \"ptc\", \"ptz\", \"tzp\")"	"c(\"\", \"tazocel\", \"tazocillin\", \"tazocin\", \"zosyn\")"			14	"g"	"character(0)"
 "PRC"		71978	"Piridicillin"	"Beta-lactams/penicillins"			""	"piridicillin"					"character(0)"
 "PRL"		157385	"Pirlimycin"	"Other antibacterials"			""	"c(\"pirlimycin\", \"pirlimycina\", \"pirlimycine\", \"pirlimycinum\", \"pirsue\")"					"character(0)"
 "PIR"	"J01MB03"	4855	"Piromidic acid"	"Quinolones"	"Quinolone antibacterials"	"Other quinolones"	""	"c(\"acide piromidique\", \"acido piromidico\", \"acidum piromidicum\", \"actrun c\", \"bactramyl\", \"enterol\", \"gastrurol\", \"panacid\", \"pirodal\", \"piromidic acid\", \"pyrido\", \"reelon\", \"septural\", \"urisept\", \"uropir\", \"zaomeal\")"	2	"g"			"character(0)"
@@ -354,6 +354,7 @@
 "POS"	"J02AC04"	468595	"Posaconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"posa"	"c(\"noxafil\", \"posaconazole\", \"posaconazole sp\", \"posconazole\")"	0.3	"g"	0.3	"g"	"c(\"53731-6\", \"80545-7\")"
 "PRA"		9802884	"Pradofloxacin"	"Quinolones"			""	"pradofloxacin"					"character(0)"
 "PRX"		71455	"Premafloxacin"	"Quinolones"			""	"premafloxacin"					"character(0)"
+"PMD"	"J04AK08"	456199	"Pretomanid"	"Antimycobacterials"	"Drugs for treatment of tuberculosis"	"Other drugs for treatment of tuberculosis"	""	""					""
 "PRM"		6446787	"Primycin"	"Macrolides/lincosamides"			""	""					""
 "PRI"	"J01FG01"	11979535	"Pristinamycin"	"Macrolides/lincosamides"	"Macrolides, lincosamides and streptogramins"	"Streptogramins"	"c(\"\", \"pris\")"	"c(\"eskalin v\", \"mikamycin\", \"mikamycine\", \"mikamycinum\", \"ostreogrycinum\", \"pristinamycine\", \"pristinamycinum\", \"stafac\", \"stafytracine\", \"staphylomycin\", \"starfac\", \"streptogramin\", \"vernamycin\", \"virgimycin\", \"virgimycine\", \"virginiamycina\", \"virginiamycine\", \"virginiamycinum\")"	2	"g"			"character(0)"
 "PRB"	"J01CE09"	5903	"Procaine benzylpenicillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase sensitive penicillins"	""	"c(\"depocillin\", \"duphapen\", \"hostacillin\", \"hydracillin\", \"jenacillin o\", \"nopcaine\", \"penicillin procaine\", \"retardillin\", \"vetspen\", \"vitablend\")"			0.6	"g"	"character(0)"
@@ -396,13 +397,13 @@
 "SIT"		461399	"Sitafloxacin"	"Quinolones"			""	"c(\"gracevit\", \"sitafloxacinisomer\")"					"character(0)"
 "SDA"	"J04AA02"	2724368	"Sodium aminosalicylate"	"Antimycobacterials"	"Drugs for treatment of tuberculosis"	"Aminosalicylic acid and derivatives"	""	"c(\"bactylan\", \"decapasil\", \"lepasen\", \"monopas\", \"nippas\", \"p.a.s. sodium\", \"pamisyl sodium\", \"parasal sodium\", \"pas sodium\", \"pasade\", \"pasnal\", \"passodico\", \"salvis\", \"sanipirol\", \"sodiopas\", \"sodium p.a.s\", \"sodium pas\", \"teebacin\", \"tubersan\")"	14	"g"	14	"g"	"character(0)"
 "SOL"		25242512	"Solithromycin"	"Macrolides/lincosamides"			""	""					""
-"SPX"	"J01MA09"	60464	"Sparfloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	"c(\"\", \"spar\")"	"c(\"esparfloxacino\", \"sparfloxacin\", \"sparfloxacine\", \"sparfloxacinum\")"	0.2	"g"			"character(0)"
+"SPX"	"J01MA09"	60464	"Sparfloxacin"	"Quinolones"	"Quinolone antibacterials"	"Fluoroquinolones"	"c(\"\", \"spa\", \"spar\")"	"c(\"esparfloxacino\", \"sparfloxacin\", \"sparfloxacine\", \"sparfloxacinum\")"	0.2	"g"			"character(0)"
 "SPT"	"J01XX04"	15541	"Spectinomycin"	"Other antibacterials"	"Other antibacterials"	"Other antibacterials"	"c(\"sc\", \"spe\", \"spec\", \"spt\")"	"c(\"actinospectacina\", \"adspec\", \"espectinomicina\", \"prospec\", \"specitinomycin\", \"spectam\", \"spectinomicina\", \"spectinomycin\", \"spectinomycin di hcl\", \"spectinomycine\", \"spectinomycinum\", \"stanilo\", \"togamycin\", \"trobicin\")"			3	"g"	"character(0)"
 "SPI"	"J01FA02"	6419898	"Spiramycin"	"Macrolides/lincosamides"	"Macrolides, lincosamides and streptogramins"	"Macrolides"	"c(\"\", \"spir\")"	"c(\"espiramicin\", \"provamycin\", \"rovamycin\", \"rovamycine\", \"sequamycin\", \"spiramycine\", \"spiramycinum\")"	3	"g"			"character(0)"
 "SPM"	"J01RA04"		"Spiramycin/metronidazole"	"Other antibacterials"	"Combinations of antibacterials"	"Combinations of antibacterials"	""	""					""
 "STR"	"J01GA02"		"Streptoduocin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Streptomycins"	""	""			1	"g"	""
-"STR1"	"J01GA01"	19649	"Streptomycin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Streptomycins"	"c(\"s\", \"str\", \"stre\")"	"c(\"agrept\", \"agrimycin\", \"chemform\", \"estreptomicina\", \"neodiestreptopab\", \"strepcen\", \"streptomicina\", \"streptomycin\", \"streptomycin a\", \"streptomycin spx\", \"streptomycin sulfate\", \"streptomycine\", \"streptomyzin\", \"vetstrep\")"			1	"g"	"4039-4"
-"STH"			"Streptomycin-high"	"Aminoglycosides"			"c(\"sthl\", \"strepto high\", \"streptomycin high\")"	""					""
+"STR1"	"J01GA01"	19649	"Streptomycin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Streptomycins"	"c(\"s\", \"stm\", \"str\", \"stre\")"	"c(\"agrept\", \"agrimycin\", \"chemform\", \"estreptomicina\", \"neodiestreptopab\", \"strepcen\", \"streptomicina\", \"streptomycin\", \"streptomycin a\", \"streptomycin spx\", \"streptomycin sulfate\", \"streptomycine\", \"streptomyzin\", \"vetstrep\")"			1	"g"	"4039-4"
+"STH"			"Streptomycin-high"	"Aminoglycosides"			"c(\"s_h\", \"sthl\", \"strepto high\", \"streptomycin high\")"	""					""
 "STI"	"J04AM01"		"Streptomycin/isoniazid"	"Antimycobacterials"	"Drugs for treatment of tuberculosis"	"Combinations of drugs for treatment of tuberculosis"	""	""					""
 "SUL"	"J01CG01"	130313	"Sulbactam"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase inhibitors"	""	"c(\"betamaze\", \"sulbactam\", \"sulbactam acid\", \"sulbactam free acid\", \"sulbactamum\")"			1	"g"	"character(0)"
 "SBC"	"J01CA16"	20055036	"Sulbenicillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	""	"c(\"kedacillina\", \"sulbenicilina\", \"sulbenicilline\", \"sulbenicillinum\")"			15	"g"	"character(0)"
@@ -478,7 +479,7 @@
 "TIA"		656958	"Tiamulin"	"Other antibacterials"			""	"c(\"denagard\", \"tiamulin\", \"tiamulin pamoate\", \"tiamulina\", \"tiamuline\", \"tiamulinum\")"					"87589-8"
 "TIC"	"J01CA13"	36921	"Ticarcillin"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	"c(\"tc\", \"ti\", \"tic\", \"tica\")"	"c(\"ticarcilina\", \"ticarcillin\", \"ticarcilline\", \"ticarcillinum\", \"ticillin\")"			15	"g"	"c(\"25254-4\", \"4054-3\", \"4055-0\")"
 "TCC"	"J01CR03"	6437075	"Ticarcillin/clavulanic acid"	"Beta-lactams/penicillins"	"Beta-lactam antibacterials, penicillins"	"Combinations of penicillins, incl. beta-lactamase inhibitors"	"c(\"t/c\", \"tcc\", \"ticl\", \"tim\", \"tlc\")"	"timentin"			15	"g"	"character(0)"
-"TGC"	"J01AA12"	54686904	"Tigecycline"	"Tetracyclines"	"Tetracyclines"	"Tetracyclines"	"c(\"tgc\", \"tige\")"	"c(\"haizheng li xing\", \"tigeciclina\", \"tigecyclin\", \"tigecycline\", \"tigecycline hydrate\", \"tigecyclinum\", \"tigilcycline\", \"tygacil\")"			0.1	"g"	"character(0)"
+"TGC"	"J01AA12"	54686904	"Tigecycline"	"Tetracyclines"	"Tetracyclines"	"Tetracyclines"	"c(\"tgc\", \"tig\", \"tige\")"	"c(\"haizheng li xing\", \"tigeciclina\", \"tigecyclin\", \"tigecycline\", \"tigecycline hydrate\", \"tigecyclinum\", \"tigilcycline\", \"tygacil\")"			0.1	"g"	"character(0)"
 "TBQ"		65592	"Tilbroquinol"	"Quinolones"			""	"c(\"tilbroquinol\", \"tilbroquinolum\")"					"character(0)"
 "TIP"		24860548	"Tildipirosin"	"Macrolides/lincosamides"			""	"c(\"tildipirosin\", \"zuprevo\")"					"character(0)"
 "TIL"		5282521	"Tilmicosin"	"Macrolides/lincosamides"			""	"c(\"micotil\", \"pulmotil\", \"tilmicosin\", \"tilmicosina\", \"tilmicosine\", \"tilmicosinum\")"					"87588-0"
@@ -490,7 +491,7 @@
 "TOB"	"J01GB01"	36294	"Tobramycin"	"Aminoglycosides"	"Aminoglycoside antibacterials"	"Other aminoglycosides"	"c(\"nn\", \"tm\", \"to\", \"tob\", \"tobr\")"	"c(\"bethkis\", \"brulamycin\", \"deoxykanamycin b\", \"distobram\", \"gernebcin\", \"gotabiotic\", \"kitabis pak\", \"nebcin\", \"nebicin\", \"nebramycin\", \"nebramycin vi\", \"obramycin\", \"sybryx\", \"tenebrimycin\", \"tenemycin\", \"tobacin\", \"tobi podhaler\", \"tobracin\", \"tobradex\", \"tobradistin\", \"tobralex\", \"tobramaxin\", \"tobramicin\", \"tobramicina\", \"tobramitsetin\", \"tobramycetin\", \"tobramycin\", \"tobramycin base\", \"tobramycin sulfate\", \"tobramycine\", \"tobramycinum\", \"tobrased\", \"tobrasone\", \"tobrex\")"			0.24	"g"	"c(\"13584-8\", \"17808-7\", \"22750-4\", \"22751-2\", \"22752-0\", \"31094-6\", \"31095-3\", \"31096-1\", \"35239-3\", \"35670-9\", \"4057-6\", \"4058-4\", \"4059-2\", \"50927-3\", \"52962-8\", \"59380-6\", \"80966-5\")"
 "TOH"			"Tobramycin-high"	"Aminoglycosides"			"c(\"tobra high\", \"tobramycin high\", \"tohl\")"	""					""
 "TFX"		5517	"Tosufloxacin"	"Quinolones"			""	"tosufloxacin"					"character(0)"
-"TMP"	"J01EA01"	5578	"Trimethoprim"	"Trimethoprims"	"Sulfonamides and trimethoprim"	"Trimethoprim and derivatives"	"c(\"t\", \"tmp\", \"tr\", \"trim\", \"w\")"	"c(\"abaprim\", \"alprim\", \"anitrim\", \"antrima\", \"antrimox\", \"bacdan\", \"bacidal\", \"bacide\", \"bacterial\", \"bacticel\", \"bactifor\", \"bactin\", \"bactoprim\", \"bactramin\", \"bactrim\", \"bencole\", \"bethaprim\", \"biosulten\", \"briscotrim\", \"chemotrin\", \"colizole\", \"colizole ds\", \"conprim\", \"cotrimel\", \"cotrimoxizole\", \"deprim\", \"dosulfin\", \"duocide\", \"esbesul\", \"espectrin\", \"euctrim\", \"exbesul\", \"fermagex\", \"fortrim\", \"idotrim\", \"ikaprim\", \"instalac\", \"kombinax\", \"lagatrim\", \"lagatrim forte\", \"lastrim\", \"lescot\", 
+"TMP"	"J01EA01"	5578	"Trimethoprim"	"Trimethoprims"	"Sulfonamides and trimethoprim"	"Trimethoprim and derivatives"	"c(\"t\", \"tmp\", \"tr\", \"tri\", \"trim\", \"w\")"	"c(\"abaprim\", \"alprim\", \"anitrim\", \"antrima\", \"antrimox\", \"bacdan\", \"bacidal\", \"bacide\", \"bacterial\", \"bacticel\", \"bactifor\", \"bactin\", \"bactoprim\", \"bactramin\", \"bactrim\", \"bencole\", \"bethaprim\", \"biosulten\", \"briscotrim\", \"chemotrin\", \"colizole\", \"colizole ds\", \"conprim\", \"cotrimel\", \"cotrimoxizole\", \"deprim\", \"dosulfin\", \"duocide\", \"esbesul\", \"espectrin\", \"euctrim\", \"exbesul\", \"fermagex\", \"fortrim\", \"idotrim\", \"ikaprim\", \"instalac\", \"kombinax\", \"lagatrim\", \"lagatrim forte\", \"lastrim\", \"lescot\", 
 \"methoprim\", \"metoprim\", \"monoprim\", \"monotrim\", \"monotrimin\", \"novotrimel\", \"omstat\", \"oraprim\", \"pancidim\", \"polytrim\", \"priloprim\", \"primosept\", \"primsol\", \"proloprim\", \"protrin\", \"purbal\", \"resprim\", \"resprim forte\", \"roubac\", \"roubal\", \"salvatrim\", \"septrin ds\", \"septrin forte\", \"septrin s\", \"setprin\", \"sinotrim\", \"stopan\", \"streptoplus\", \"sugaprim\", \"sulfamar\", \"sulfamethoprim\", \"sulfoxaprim\", \"sulthrim\", \"sultrex\", \"syraprim\", \"tiempe\", \"tmp smx\", \"toprim\", \"trimanyl\", \"trimethioprim\", \"trimethopim\", 
 \"trimethoprim\", \"trimethoprime\", \"trimethoprimum\", \"trimethopriom\", \"trimetoprim\", \"trimetoprima\", \"trimexazole\", \"trimexol\", \"trimezol\", \"trimogal\", \"trimono\", \"trimopan\", \"trimpex\", \"triprim\", \"trisul\", \"trisulcom\", \"trisulfam\", \"trisural\", \"uretrim\", \"urobactrim\", \"utetrin\", \"velaten\", \"wellcoprim\", \"wellcoprin\", \"xeroprim\", \"zamboprim\")"	0.4	"g"	0.4	"g"	"c(\"11005-6\", \"17747-7\", \"25273-4\", \"32342-8\", \"4079-0\", \"4080-8\", \"4081-6\", \"55584-7\", \"80552-3\", \"80973-1\")"
 "SXT"	"J01EE01"	358641	"Trimethoprim/sulfamethoxazole"	"Trimethoprims"	"Sulfonamides and trimethoprim"	"Combinations of sulfonamides and trimethoprim, incl. derivatives"	"c(\"cot\", \"cotrim\", \"sxt\", \"t/s\", \"trsu\", \"trsx\", \"ts\")"	"c(\"bactrim\", \"bactrimel\", \"belcomycine\", \"colimycin\", \"colimycin sulphate\", \"colisticin\", \"colistimethate\", \"colistimethate sodium\", \"colistin sulfate\", \"colistin sulphate\", \"colomycin\", \"coly-mycin\", \"cotrimazole\", \"cotrimoxazole\", \"polymyxin e\", \"polymyxin e. sulfate\", \"promixin\", \"septra\", \"totazina\")"					"character(0)"
@@ -505,6 +506,6 @@
 "VAM"			"Vancomycin-macromethod"	"Glycopeptides"			""	""					""
 "VIO"		135398671	"Viomycin"	"Antimycobacterials"			""	"c(\"celiomycin\", \"florimycin\", \"floromycin\", \"viomicina\", \"viomycin\", \"viomycine\", \"viomycinum\")"					"character(0)"
 "VIR"		11979535	"Virginiamycine"	"Other antibacterials"			""	"c(\"eskalin v\", \"mikamycin\", \"mikamycine\", \"mikamycinum\", \"ostreogrycinum\", \"pristinamycine\", \"pristinamycinum\", \"stafac\", \"stafytracine\", \"staphylomycin\", \"starfac\", \"streptogramin\", \"vernamycin\", \"virgimycin\", \"virgimycine\", \"virginiamycina\", \"virginiamycine\", \"virginiamycinum\")"					"character(0)"
-"VOR"	"J02AC03"	71616	"Voriconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"vori"	"c(\"pfizer\", \"vfend i.v.\", \"voriconazol\", \"voriconazole\", \"voriconazolum\", \"vorikonazole\")"	0.4	"g"	0.4	"g"	"c(\"38370-3\", \"53902-3\", \"73676-9\", \"80553-1\", \"80651-3\")"
+"VOR"	"J02AC03"	71616	"Voriconazole"	"Antifungals/antimycotics"	"Antimycotics for systemic use"	"Triazole derivatives"	"c(\"vori\", \"vrc\")"	"c(\"pfizer\", \"vfend i.v.\", \"voriconazol\", \"voriconazole\", \"voriconazolum\", \"vorikonazole\")"	0.4	"g"	0.4	"g"	"c(\"38370-3\", \"53902-3\", \"73676-9\", \"80553-1\", \"80651-3\")"
 "XBR"	"J01XX02"	72144	"Xibornol"	"Other antibacterials"	"Other antibacterials"	"Other antibacterials"	""	"c(\"bactacine\", \"bracen\", \"nanbacine\", \"xibornol\", \"xibornolo\", \"xibornolum\")"					"character(0)"
 "ZID"		77846445	"Zidebactam"	"Other antibacterials"			""	"zidebactam"					"character(0)"
--- a/data-raw/antibiotics.xlsx
+++ b/data-raw/antibiotics.xlsx
--- a/data-raw/dosage.dta
+++ b/data-raw/dosage.dta
--- a/data-raw/dosage.md5
+++ b/data-raw/dosage.md5
@@ -0,0 +1 @@
+58d6a0589aea598420e37045fb04a5ae
--- a/data-raw/dosage.rds
+++ b/data-raw/dosage.rds
--- a/data-raw/dosage.sas
+++ b/data-raw/dosage.sas
--- a/data-raw/dosage.sav
+++ b/data-raw/dosage.sav
--- a/data-raw/dosage.txt
+++ b/data-raw/dosage.txt
@@ -0,0 +1,170 @@
+"ab"	"name"	"type"	"dose"	"dose_times"	"administration"	"notes"	"original_txt"	"eucast_version"
+"AMK"	"Amikacin"	"standard_dosage"	"25-30 mg/kg"	1	"iv"	""	"25-30 mg/kg x 1 iv"	11
+"AMX"	"Amoxicillin"	"high_dosage"	"2 g"	6	"iv"	""	"2 g x 6 iv"	11
+"AMX"	"Amoxicillin"	"standard_dosage"	"1 g"	3	"iv"	""	"1 g x 3-4 iv"	11
+"AMX"	"Amoxicillin"	"high_dosage"	"0.75-1 g"	3	"oral"	""	"0.75-1 g x 3 oral"	11
+"AMX"	"Amoxicillin"	"standard_dosage"	"0.5 g"	3	"oral"	""	"0.5 g x 3 oral"	11
+"AMX"	"Amoxicillin"	"uncomplicated_uti"	"0.5 g"	3	"oral"	""	"0.5 g x 3 oral"	11
+"AMC"	"Amoxicillin/clavulanic acid"	"high_dosage"	"2 g + 0.2 g"	3	"iv"	""	"(2 g amoxicillin + 0.2 g clavulanic acid) x 3 iv"	11
+"AMC"	"Amoxicillin/clavulanic acid"	"standard_dosage"	"1 g + 0.2 g"	3	"iv"	""	"(1 g amoxicillin + 0.2 g clavulanic acid) x 3-4 iv"	11
+"AMC"	"Amoxicillin/clavulanic acid"	"high_dosage"	"0.875 g + 0.125 g"	3	"oral"	""	"(0.875 g amoxicillin + 0.125 g clavulanic acid) x 3 oral"	11
+"AMC"	"Amoxicillin/clavulanic acid"	"standard_dosage"	"0.5 g + 0.125 g"	3	"oral"	""	"(0.5 g amoxicillin + 0.125 g clavulanic acid) x 3 oral"	11
+"AMC"	"Amoxicillin/clavulanic acid"	"uncomplicated_uti"	"0.5 g + 0.125 g"	3	"oral"	""	"(0.5 g amoxicillin + 0.125 g clavulanic acid) x 3 oral"	11
+"AMP"	"Ampicillin"	"high_dosage"	"2 g"	4	"iv"	""	"2 g x 4 iv"	11
+"AMP"	"Ampicillin"	"standard_dosage"	"2 g"	3	"iv"	""	"2 g x 3 iv"	11
+"SAM"	"Ampicillin/sulbactam"	"high_dosage"	"2 g + 1 g"	4	"iv"	""	"(2 g ampicillin + 1 g sulbactam) x 4 iv"	11
+"SAM"	"Ampicillin/sulbactam"	"standard_dosage"	"2 g + 1 g"	3	"iv"	""	"(2 g ampicillin + 1 g sulbactam) x 3 iv"	11
+"AZM"	"Azithromycin"	"standard_dosage"	"0.5 g"	1	"iv"	""	"0.5 g x 1 iv"	11
+"AZM"	"Azithromycin"	"standard_dosage"	"0.5 g"	1	"oral"	""	"0.5 g x 1 oral"	11
+"ATM"	"Aztreonam"	"high_dosage"	"2 g"	4	"iv"	""	"2 g x 4 iv"	11
+"ATM"	"Aztreonam"	"standard_dosage"	"1 g"	3	"iv"	""	"1 g x 3 iv"	11
+"PEN"	"Benzylpenicillin"	"high_dosage"	"1.2 g"	4	"iv"	""	"1.2 g (2 MU) x 4-6 iv"	11
+"PEN"	"Benzylpenicillin"	"standard_dosage"	"0.6 g"	4	"iv"	""	"0.6 g (1 MU) x 4 iv"	11
+"CEC"	"Cefaclor"	"high_dosage"	"1 g"	3	"oral"	""	"1 g x 3 oral"	11
+"CEC"	"Cefaclor"	"standard_dosage"	"0.25-0.5 g"	3	"oral"	""	"0.25-0.5 g x 3 oral"	11
+"CFR"	"Cefadroxil"	"standard_dosage"	"0.5-1 g"	2	"oral"	""	"0.5-1 g x 2 oral"	11
+"CFR"	"Cefadroxil"	"uncomplicated_uti"	"0.5-1 g"	2	"oral"	""	"0.5-1 g x 2 oral"	11
+"CZO"	"Cefazolin"	"high_dosage"	"2 g"	3	"iv"	""	"2 g x 3 iv"	11
+"CZO"	"Cefazolin"	"standard_dosage"	"1 g"	3	"iv"	""	"1 g x 3 iv"	11
+"FEP"	"Cefepime"	"high_dosage"	"2 g"	3	"iv"	""	"2 g x 3 iv"	11
+"FEP"	"Cefepime"	"standard_dosage"	"2 g"	2	"iv"	""	"2 g x 2 iv"	11
+"FDC"	"Cefiderocol"	"standard_dosage"	"2 g"	3	"iv"	"over 3 hours"	"2 g x 3 iv over 3 hours"	11
+"CFM"	"Cefixime"	"standard_dosage"	"0.2-0.4 g"	2	"oral"	""	"0.2-0.4 g x 2 oral"	11
+"CFM"	"Cefixime"	"uncomplicated_uti"	"0.2-0.4 g"	2	"oral"	""	"0.2-0.4 g x 2 oral"	11
+"CTX"	"Cefotaxime"	"high_dosage"	"2 g"	3	"iv"	""	"2 g x 3 iv"	11
+"CTX"	"Cefotaxime"	"standard_dosage"	"1 g"	3	"iv"	""	"1 g x 3 iv"	11
+"CPD"	"Cefpodoxime"	"standard_dosage"	"0.1-0.2 g"	2	"oral"	""	"0.1-0.2 g x 2 oral"	11
+"CPD"	"Cefpodoxime"	"uncomplicated_uti"	"0.1-0.2 g"	2	"oral"	""	"0.1-0.2 g x 2 oral"	11
+"CPT"	"Ceftaroline"	"high_dosage"	"0.6 g"	3	"iv"	"over 2 hours"	"0.6 g x 3 iv over 2 hours"	11
+"CPT"	"Ceftaroline"	"standard_dosage"	"0.6 g"	2	"iv"	"over 1 hour"	"0.6 g x 2 iv over 1 hour"	11
+"CAZ"	"Ceftazidime"	"high_dosage"	"1 g"	6	"iv"	""	"1 g x 6 iv"	11
+"CAZ"	"Ceftazidime"	"standard_dosage"	"1 g"	3	"iv"	""	"1 g x 3 iv"	11
+"CZA"	"Ceftazidime/avibactam"	"standard_dosage"	"2 g + 0.5 g"	3	"iv"	"over 2 hours"	"(2 g ceftazidime + 0.5 g avibactam) x 3 iv over 2 hours"	11
+"CTB"	"Ceftibuten"	"standard_dosage"	"0.4 g"	1	"oral"	""	"0.4 g x 1 oral"	11
+"BPR"	"Ceftobiprole"	"standard_dosage"	"0.5 g"	3	"iv"	"over 2 hours"	"0.5 g x 3 iv over 2 hours"	11
+"CZT"	"Ceftolozane/tazobactam"	"standard_dosage"	"1 g + 0.5 g"	3	"iv"	"over 1 hour"	"(1 g ceftolozane + 0.5 g tazobactam) x 3 iv over 1 hour"	11
+"CZT"	"Ceftolozane/tazobactam"	"standard_dosage"	"2 g + 1 g"	3	"iv"	"over 1 hour"	"(2 g ceftolozane + 1 g tazobactam) x 3 iv over 1 hour"	11
+"CRO"	"Ceftriaxone"	"high_dosage"	"4 g"	1	"iv"	""	"4 g x 1 iv"	11
+"CRO"	"Ceftriaxone"	"standard_dosage"	"2 g"	1	"iv"	""	"2 g x 1 iv"	11
+"CXM"	"Cefuroxime"	"high_dosage"	"1.5 g"	3	"iv"	""	"1.5 g x 3 iv"	11
+"CXM"	"Cefuroxime"	"standard_dosage"	"0.75 g"	3	"iv"	""	"0.75 g x 3 iv"	11
+"CXM"	"Cefuroxime"	"high_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2 oral"	11
+"CXM"	"Cefuroxime"	"standard_dosage"	"0.25 g"	2	"oral"	""	"0.25 g x 2 oral"	11
+"CXM"	"Cefuroxime"	"uncomplicated_uti"	"0.25 g"	2	"oral"	""	"0.25 g x 2 oral"	11
+"LEX"	"Cephalexin"	"standard_dosage"	"0.25-1 g"	2	"oral"	""	"0.25-1 g x 2-3 oral"	11
+"LEX"	"Cephalexin"	"uncomplicated_uti"	"0.25-1 g"	2	"oral"	""	"0.25-1 g x 2-3 oral"	11
+"CHL"	"Chloramphenicol"	"high_dosage"	"2 g"	4	"iv"	""	"2 g x 4 iv"	11
+"CHL"	"Chloramphenicol"	"standard_dosage"	"1 g"	4	"iv"	""	"1 g x 4 iv"	11
+"CHL"	"Chloramphenicol"	"high_dosage"	"2 g"	4	"oral"	""	"2 g x 4 oral"	11
+"CHL"	"Chloramphenicol"	"standard_dosage"	"1 g"	4	"oral"	""	"1 g x 4 oral"	11
+"CIP"	"Ciprofloxacin"	"high_dosage"	"0.4 g"	3	"iv"	""	"0.4 g x 3 iv"	11
+"CIP"	"Ciprofloxacin"	"standard_dosage"	"0.4 g"	2	"iv"	""	"0.4 g x 2 iv"	11
+"CIP"	"Ciprofloxacin"	"high_dosage"	"0.75 g"	2	"oral"	""	"0.75 g x 2 oral"	11
+"CIP"	"Ciprofloxacin"	"standard_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2 oral"	11
+"CLR"	"Clarithromycin"	"high_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2 oral"	11
+"CLR"	"Clarithromycin"	"standard_dosage"	"0.25 g"	2	"oral"	""	"0.25 g x 2 oral"	11
+"CLI"	"Clindamycin"	"high_dosage"	"0.9 g"	3	"iv"	""	"0.9 g x 3 iv"	11
+"CLI"	"Clindamycin"	"standard_dosage"	"0.6 g"	3	"iv"	""	"0.6 g x 3 iv"	11
+"CLI"	"Clindamycin"	"high_dosage"	"0.3 g"	4	"oral"	""	"0.3 g x 4 oral"	11
+"CLI"	"Clindamycin"	"standard_dosage"	"0.3 g"	2	"oral"	""	"0.3 g x 2 oral"	11
+"CLO"	"Cloxacillin"	"high_dosage"	"2 g"	6	"iv"	""	"2 g x 6 iv"	11
+"CLO"	"Cloxacillin"	"standard_dosage"	"1 g"	4	"iv"	""	"1 g x 4 iv"	11
+"CLO"	"Cloxacillin"	"high_dosage"	"1 g"	4	"oral"	""	"1 g x 4 oral"	11
+"CLO"	"Cloxacillin"	"standard_dosage"	"0.5 g"	4	"oral"	""	"0.5 g x 4 oral"	11
+"COL"	"Colistin"	"standard_dosage"	"4.5 MU"	2	"iv"	"loading dose of 9 MU"	"4.5 MU x 2 iv with a loading dose of 9 MU"	11
+"DAL"	"Dalbavancin"	"standard_dosage"	"1 g"	1	"iv"	"over 30 minutes on day 8"	"1 g x 1 iv over 30 minutes on day 1  If needed, 0.5 g x 1 iv over 30 minutes on day 8"	11
+"DAP"	"Daptomycin"	"standard_dosage"	"4 mg/kg"	1	"iv"	""	"4 mg/kg x 1 iv"	11
+"DAP"	"Daptomycin"	"standard_dosage"	"6 mg/kg"	1	"iv"	""	"6 mg/kg x 1 iv"	11
+"DFX"	"Delafloxacin"	"standard_dosage"	"0.3 g"	2	"iv"	""	"0.3 g x 2 iv"	11
+"DFX"	"Delafloxacin"	"standard_dosage"	"0.45 g"	2	"oral"	""	"0.45 g x 2 oral"	11
+"DIC"	"Dicloxacillin"	"high_dosage"	"2 g"	6	"iv"	""	"2 g x 6 iv"	11
+"DIC"	"Dicloxacillin"	"standard_dosage"	"1 g"	4	"iv"	""	"1 g x 4 iv"	11
+"DIC"	"Dicloxacillin"	"high_dosage"	"2 g"	4	"oral"	""	"2 g x 4 oral"	11
+"DIC"	"Dicloxacillin"	"standard_dosage"	"0.5-1 g"	4	"oral"	""	"0.5-1 g x 4 oral"	11
+"DOR"	"Doripenem"	"high_dosage"	"1 g"	3	"iv"	"over 1 hour"	"1 g x 3 iv over 1 hour"	11
+"DOR"	"Doripenem"	"standard_dosage"	"0.5 g"	3	"iv"	"over 1 hour"	"0.5 g x 3 iv over 1 hour"	11
+"DOX"	"Doxycycline"	"high_dosage"	"0.2 g"	1	"oral"	""	"0.2 g x 1 oral"	11
+"DOX"	"Doxycycline"	"standard_dosage"	"0.1 g"	1	"oral"	""	"0.1 g x 1 oral"	11
+"ERV"	"Eravacycline"	"standard_dosage"	"1 mg/kg"	2	"iv"	""	"1 mg/kg x 2 iv"	11
+"ETP"	"Ertapenem"	"standard_dosage"	"1 g"	1	"iv"	"over 30 minutes"	"1 g x 1 iv over 30 minutes"	11
+"ERY"	"Erythromycin"	"high_dosage"	"1 g"	4	"iv"	""	"1 g x 4 iv"	11
+"ERY"	"Erythromycin"	"standard_dosage"	"0.5 g"	2	"iv"	""	"0.5 g x 2-4 iv"	11
+"ERY"	"Erythromycin"	"high_dosage"	"1 g"	4	"oral"	""	"1 g x 4 oral"	11
+"ERY"	"Erythromycin"	"standard_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2-4 oral"	11
+"FDX"	"Fidaxomicin"	"standard_dosage"	"0.2 g"	2	"oral"	""	"0.2 g x 2 oral"	11
+"FLC"	"Flucloxacillin"	"high_dosage"	"2 g"	6	"iv"	""	"2 g x 6 iv"	11
+"FLC"	"Flucloxacillin"	"standard_dosage"	"2 g"	4	"iv"	""	"2 g x 4 iv (or 1 g x 6 iv)"	11
+"FLC"	"Flucloxacillin"	"high_dosage"	"1 g"	4	"oral"	""	"1 g x 4 oral"	11
+"FLC"	"Flucloxacillin"	"standard_dosage"	"1 g"	3	"oral"	""	"1 g x 3 oral"	11
+"FOS"	"Fosfomycin"	"high_dosage"	"8 g"	3	"iv"	""	"8 g x 3 iv"	11
+"FOS"	"Fosfomycin"	"standard_dosage"	"4 g"	3	"iv"	""	"4 g x 3 iv"	11
+"FUS"	"Fusidic acid"	"high_dosage"	"0.5 g"	3	"iv"	""	"0.5 g x 3 iv"	11
+"FUS"	"Fusidic acid"	"standard_dosage"	"0.5 g"	2	"iv"	""	"0.5 g x 2 iv"	11
+"FUS"	"Fusidic acid"	"high_dosage"	"0.5 g"	3	"oral"	""	"0.5 g x 3 oral"	11
+"FUS"	"Fusidic acid"	"standard_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2 oral"	11
+"GEN"	"Gentamicin"	"standard_dosage"	"6-7 mg/kg"	1	"iv"	""	"6-7 mg/kg x 1 iv"	11
+"IPM"	"Imipenem"	"high_dosage"	"1 g"	4	"iv"	"over 30 minutes"	"1 g x 4 iv over 30 minutes"	11
+"IPM"	"Imipenem"	"standard_dosage"	"0.5 g"	4	"iv"	"over 30 minutes"	"0.5 g x 4 iv over 30 minutes"	11
+"IMR"	"Imipenem/relebactam"	"standard_dosage"	"0.5 g + 0.25 g"	4	"iv"	"over 30 minutes"	"(0.5 g imipenem + 0.25 g relebactam) x 4 iv over 30 minutes"	11
+"IMR"	"Imipenem/relebactam"	"standard_dosage"	"0.5 g + 0.25 g"	4	"iv"	"over 30 minutes"	"(0.5 g imipenem + 0.25 g relebactam) x 4 iv over 30 minutes"	11
+"LMU"	"Lefamulin"	"standard_dosage"	"0.15 g"	2	"iv"	"or 0.6 g x 2 oral"	"0.15 g x 2 iv or 0.6 g x 2 oral"	11
+"LMU"	"Lefamulin"	"standard_dosage"	"0.6 g"	2	"oral"	""	"0.6 g x 2 oral"	11
+"LVX"	"Levofloxacin"	"high_dosage"	"0.5 g"	2	"iv"	""	"0.5 g x 2 iv"	11
+"LVX"	"Levofloxacin"	"standard_dosage"	"0.5 g"	1	"iv"	""	"0.5 g x 1 iv"	11
+"LVX"	"Levofloxacin"	"high_dosage"	"0.5 g"	2	"oral"	""	"0.5 g x 2 oral"	11
+"LVX"	"Levofloxacin"	"standard_dosage"	"0.5 g"	1	"oral"	""	"0.5 g x 1 oral"	11
+"LNZ"	"Linezolid"	"standard_dosage"	"0.6 g"	2	"iv"	""	"0.6 g x 2 iv"	11
+"LNZ"	"Linezolid"	"standard_dosage"	"0.6 g"	2	"oral"	""	"0.6 g x 2 oral"	11
+"MEM"	"Meropenem"	"high_dosage"	"2 g"	3	"iv"	"over 3 hours"	"2 g x 3 iv over 3 hours"	11
+"MEM"	"Meropenem"	"standard_dosage"	"1 g"	3	"iv"	"over 30 minutes"	"1 g x 3 iv over 30 minutes"	11
+"MEV"	"Meropenem/vaborbactam"	"standard_dosage"	"2 g + 2 g"	3	"iv"	"over 3 hours"	"(2 g meropenem + 2 g vaborbactam) x 3 iv over 3 hours"	11
+"MTR"	"Metronidazole"	"high_dosage"	"0.5 g"	3	"iv"	""	"0.5 g x 3 iv"	11
+"MTR"	"Metronidazole"	"standard_dosage"	"0.4 g"	3	"iv"	""	"0.4 g x 3 iv"	11
+"MTR"	"Metronidazole"	"high_dosage"	"0.5 g"	3	"oral"	""	"0.5 g x 3 oral"	11
+"MTR"	"Metronidazole"	"standard_dosage"	"0.4 g"	3	"oral"	""	"0.4 g x 3 oral"	11
+"MNO"	"Minocycline"	"standard_dosage"	"0.1 g"	2	"oral"	""	"0.1 g x 2 oral"	11
+"MFX"	"Moxifloxacin"	"standard_dosage"	"0.4 g"	1	"iv"	""	"0.4 g x 1 iv"	11
+"MFX"	"Moxifloxacin"	"standard_dosage"	"0.4 g"	1	"oral"	""	"0.4 g x 1 oral"	11
+"OFX"	"Ofloxacin"	"high_dosage"	"0.4 g"	2	"iv"	""	"0.4 g x 2 iv"	11
+"OFX"	"Ofloxacin"	"standard_dosage"	"0.2 g"	2	"iv"	""	"0.2 g x 2 iv"	11
+"OFX"	"Ofloxacin"	"high_dosage"	"0.4 g"	2	"oral"	""	"0.4 g x 2 oral"	11
+"OFX"	"Ofloxacin"	"standard_dosage"	"0.2 g"	2	"oral"	""	"0.2 g x 2 oral"	11
+"ORI"	"Oritavancin"	"standard_dosage"	"1.2 g"	1	"iv"	""	"1.2 g x 1 (single dose) iv over 3 hours"	11
+"OXA"	"Oxacillin"	"high_dosage"	"1 g"	6	"iv"	""	"1 g x 6 iv"	11
+"OXA"	"Oxacillin"	"standard_dosage"	"1 g"	4	"iv"	""	"1 g x 4 iv"	11
+"PHN"	"Phenoxymethylpenicillin"	"standard_dosage"	"0.5-2 g"	3	"oral"	""	"0.5-2 g x 3-4 oral"	11
+"PIP"	"Piperacillin"	"high_dosage"	"4 g"	4	"iv"	""	"4 g x 4 iv by extended 3-hour infusion"	11
+"PIP"	"Piperacillin"	"standard_dosage"	"4 g"	4	"iv"	""	"4 g x 4 iv"	11
+"TZP"	"Piperacillin/tazobactam"	"high_dosage"	"4 g + 0.5 g"	4	"iv"	""	"(4 g piperacillin + 0.5 g tazobactam) x 4 iv by extended 3-hour infusion"	11
+"TZP"	"Piperacillin/tazobactam"	"standard_dosage"	"4 g + 0.5 g"	4	"iv"	""	"(4 g piperacillin + 0.5 g tazobactam) x 4 iv or x 3 by extended 4-hour infusion"	11
+"QDA"	"Quinupristin/dalfopristin"	"high_dosage"	"7.5 mg/kg"	3	"iv"	""	"7.5 mg/kg x 3 iv"	11
+"QDA"	"Quinupristin/dalfopristin"	"standard_dosage"	"7.5 mg/kg"	2	"iv"	""	"7.5 mg/kg x 2 iv"	11
+"RIF"	"Rifampicin"	"high_dosage"	"0.6 g"	2	"iv"	""	"0.6 g x 2 iv"	11
+"RIF"	"Rifampicin"	"standard_dosage"	"0.6 g"	1	"iv"	""	"0.6 g x 1 iv"	11
+"RIF"	"Rifampicin"	"high_dosage"	"0.6 g"	2	"oral"	""	"0.6 g x 2 oral"	11
+"RIF"	"Rifampicin"	"standard_dosage"	"0.6 g"	1	"oral"	""	"0.6 g x 1 oral"	11
+"RXT"	"Roxithromycin"	"standard_dosage"	"0.15 g"	2	"oral"	""	"0.15 g x 2 oral"	11
+"SPT"	"Spectinomycin"	"standard_dosage"	"2 g"	1	"im"	""	"2 g x 1 im"	11
+"TZD"	"Tedizolid"	"standard_dosage"	"0.2 g"	1	"iv"	""	"0.2 g x 1 iv"	11
+"TZD"	"Tedizolid"	"standard_dosage"	"0.2 g"	1	"oral"	""	"0.2 g x 1 oral"	11
+"TEC"	"Teicoplanin"	"high_dosage"	"0.8 g"	1	"iv"	""	"0.8 g x 1 iv"	11
+"TEC"	"Teicoplanin"	"standard_dosage"	"0.4 g"	1	"iv"	""	"0.4 g x 1 iv"	11
+"TLV"	"Telavancin"	"standard_dosage"	"10 mg/kg"	1	"iv"	"over 1 hour"	"10 mg/kg x 1 iv over 1 hour"	11
+"TLT"	"Telithromycin"	"standard_dosage"	"0.8 g"	1	"oral"	""	"0.8 g x 1 oral"	11
+"TEM"	"Temocillin"	"high_dosage"	"2 g"	3	"iv"	""	"2 g x 3 iv"	11
+"TEM"	"Temocillin"	"standard_dosage"	"2 g"	2	"iv"	""	"2 g x 2 iv"	11
+"TCY"	"Tetracycline"	"high_dosage"	"0.5 g"	4	"oral"	""	"0.5 g x 4 oral"	11
+"TCY"	"Tetracycline"	"standard_dosage"	"0.25 g"	4	"oral"	""	"0.25 g x 4 oral"	11
+"TIC"	"Ticarcillin"	"high_dosage"	"3 g"	6	"iv"	""	"3 g x 6 iv"	11
+"TIC"	"Ticarcillin"	"standard_dosage"	"3 g"	4	"iv"	""	"3 g x 4 iv"	11
+"TCC"	"Ticarcillin/clavulanic acid"	"high_dosage"	"3 g + 0.1 g"	6	"iv"	""	"(3 g ticarcillin + 0.1 g clavulanic acid) x 6 iv"	11
+"TCC"	"Ticarcillin/clavulanic acid"	"standard_dosage"	"3 g + 0.1-0.2 g"	4	"iv"	""	"(3 g ticarcillin + 0.1-0.2 g clavulanic acid) x 4 iv"	11
+"TGC"	"Tigecycline"	"standard_dosage"	"0.1 g"			"loading dose followed by 50 mg x 2 iv"	"0.1 g loading dose followed by 50 mg x 2 iv"	11
+"TOB"	"Tobramycin"	"standard_dosage"	"6-7 mg/kg"	1	"iv"	""	"6-7 mg/kg x 1 iv"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"high_dosage"	"0.24 g + 1.2 g"	2	"oral"	""	"(0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 oral"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"high_dosage"	"0.24 g + 1.2 g"	2	"oral"	""	"(0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 oral or (0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 iv"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"standard_dosage"	"0.16 g + 0.8 g"	2	"oral"	""	"(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"standard_dosage"	"0.16 g + 0.8 g"	2	"oral"	""	"(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral or (0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 iv"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"uncomplicated_uti"	"0.16 g + 0.8 g"	2	"oral"	""	"(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral"	11
+"SXT"	"Trimethoprim/sulfamethoxazole"	"uncomplicated_uti"	"0.16 g + 0.8 g"	2	"oral"	""	"(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral"	11
+"VAN"	"Vancomycin"	"standard_dosage"	"1 g"	2	"iv"	""	"1 g x 2 iv or 2 g x 1 by continuous infusion"	11
--- a/data-raw/dosage.xlsx
+++ b/data-raw/dosage.xlsx
--- a/data-raw/eucast_rules.tsv
+++ b/data-raw/eucast_rules.tsv
@@ -1,7 +1,7 @@
 # -------------------------------------------------------------------------------------------------------------------------------
 # For editing this EUCAST reference file, these values can all be used for targeting antibiotics:
-# 'all_betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_1st', 'cephalosporins_2nd', 'cephalosporins_except_CAZ',
-# 'fluoroquinolones', 'glycopeptides', 'lincosamides', 'lipoglycopeptides', 'macrolides', 'oxazolidinones', 'polymyxins', 'streptogramins', 'tetracyclines', 'ureidopenicillins',
+# 'betalactams', 'aminoglycosides', 'aminopenicillins', 'carbapenems', 'cephalosporins', 'cephalosporins_1st', 'cephalosporins_2nd', 'cephalosporins_3rd', 'cephalosporins_except_CAZ',
+# 'fluoroquinolones', 'glycopeptides', 'glycopeptides_except_lipo', 'lincosamides', 'lipoglycopeptides', 'macrolides', 'oxazolidinones', 'polymyxins', 'streptogramins', 'tetracyclines', 'tetracyclines_except_TGC', '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".
 # The like.is.one_of column must be 'like' or 'is' or 'one_of' ('like' will read the 'this_value' column as regular expression)
@@ -14,7 +14,7 @@ order	is	Enterobacterales	AMP	I	AMX	I	Enterobacterales (Order)	Breakpoints	10
 order	is	Enterobacterales	AMP	R	AMX	R	Enterobacterales (Order)	Breakpoints	10	
 genus	is	Staphylococcus	PEN, FOX	S	AMP, AMX, PIP, TIC	S	Staphylococcus	Breakpoints	10	
 genus	is	Staphylococcus	PEN, FOX	R, S	OXA, FLC	S	Staphylococcus	Breakpoints	10	
-genus	is	Staphylococcus	FOX	R	all_betalactams	R	Staphylococcus	Breakpoints	10	
+genus	is	Staphylococcus	FOX	R	betalactams	R	Staphylococcus	Breakpoints	10	
 genus_species	is	Staphylococcus saprophyticus	AMP	S	AMX, AMC, PIP, TZP	S	Staphylococcus	Breakpoints	10	
 genus	is	Staphylococcus	FOX	S	carbapenems, cephalosporins_except_CAZ	S	Staphylococcus	Breakpoints	10	
 genus	is	Staphylococcus	FOX	I	carbapenems, cephalosporins_except_CAZ	I	Staphylococcus	Breakpoints	10	
@@ -115,7 +115,116 @@ genus_species	is	Kingella kingae	TCY	R	DOX	R	Kingella kingae	Breakpoints	10
 genus_species	is	Burkholderia pseudomallei	TCY	S	DOX	S	Burkholderia pseudomallei	Breakpoints	10	
 genus_species	is	Burkholderia pseudomallei	TCY	I	DOX	I	Burkholderia pseudomallei	Breakpoints	10	
 genus_species	is	Burkholderia pseudomallei	TCY	R	DOX	R	Burkholderia pseudomallei	Breakpoints	10	
-order	is	Enterobacterales			PEN, glycopeptides, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+order	is	Enterobacterales	AMP	S	AMX	S	Enterobacterales (Order)	Breakpoints	11	
+order	is	Enterobacterales	AMP	I	AMX	I	Enterobacterales (Order)	Breakpoints	11	
+order	is	Enterobacterales	AMP	R	AMX	R	Enterobacterales (Order)	Breakpoints	11	
+genus	is	Staphylococcus	PEN, FOX	S	AMP, AMX, PIP, TIC	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	PEN, FOX	R, S	OXA, FLC	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	FOX	R	betalactams	R	Staphylococcus	Breakpoints	11	
+genus_species	is	Staphylococcus saprophyticus	AMP	S	AMX, AMC, PIP, TZP	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	FOX	S	carbapenems, cephalosporins_except_CAZ	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	FOX	I	carbapenems, cephalosporins_except_CAZ	I	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	FOX	R	carbapenems, cephalosporins_except_CAZ	R	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	NOR	S	CIP, LVX, MFX, OFX	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	ERY	S	AZM, CLR, RXT	S	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	ERY	I	AZM, CLR, RXT	I	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	ERY	R	AZM, CLR, RXT	R	Staphylococcus	Breakpoints	11	
+genus	is	Staphylococcus	TCY	S	DOX, MNO	S	Staphylococcus	Breakpoints	11	
+genus	is	Enterococcus	AMP	S	AMX, AMC, PIP, TZP	S	Enterococcus	Breakpoints	11	
+genus	is	Enterococcus	AMP	I	AMX, AMC, PIP, TZP	I	Enterococcus	Breakpoints	11	
+genus	is	Enterococcus	AMP	R	AMX, AMC, PIP, TZP	R	Enterococcus	Breakpoints	11	
+genus	is	Enterococcus	NOR	S	CIP, LVX	S	Enterococcus	Breakpoints	11	
+genus	is	Enterococcus	NOR	I	CIP, LVX	I	Enterococcus	Breakpoints	11	
+genus	is	Enterococcus	NOR	R	CIP, LVX	R	Enterococcus	Breakpoints	11	
+genus_species	one_of	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	PEN	S	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	S	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	PEN	I	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	I	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	PEN	R	aminopenicillins, ureidopenicillins, cephalosporins_except_CAZ, carbapenems, FLC, AMC	R	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	NOR	S	MFX	S	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	NOR	S	LVX	I	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	ERY	S	AZM, CLR, RXT	S	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	ERY	I	AZM, CLR, RXT	I	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	ERY	R	AZM, CLR, RXT	R	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	like	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	TCY	S	DOX, MNO	S	Streptococcus groups A, B, C, G	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	PEN	S	AMP, AMX, AMC, PIP, TZP	S	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	AMP	S	AMX, AMC, PIP, TZP	S	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	AMP	I	AMX, AMC, PIP, TZP	I	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	AMP	R	AMX, AMC, PIP, TZP	R	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	NOR	S	MFX	S	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	NOR	S	LVX	I	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	ERY	S	AZM, CLR, RXT	S	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	ERY	I	AZM, CLR, RXT	I	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	ERY	R	AZM, CLR, RXT	R	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	is	Streptococcus pneumoniae	TCY	S	DOX, MNO	S	Streptococcus pneumoniae	Breakpoints	11	
+genus_species	like	^Streptococcus (anginosus|australis|bovis|constellatus|cristatus|equinus|gallolyticus|gordonii|infantarius|infantis|intermedius|mitis|mutans|oligofermentans|oralis|parasanguinis|peroris|pseudopneumoniae|salivarius|sanguinis|sinensis|sobrinus|thermophilus|vestibularis|viridans)$	PEN	S	AMP, AMX, AMC, PIP, TZP	S	Viridans group streptococci	Breakpoints	11	
+genus_species	like	^Streptococcus (anginosus|australis|bovis|constellatus|cristatus|equinus|gallolyticus|gordonii|infantarius|infantis|intermedius|mitis|mutans|oligofermentans|oralis|parasanguinis|peroris|pseudopneumoniae|salivarius|sanguinis|sinensis|sobrinus|thermophilus|vestibularis|viridans)$	AMP	S	AMX, AMC, PIP, TZP	S	Viridans group streptococci	Breakpoints	11	
+genus_species	like	^Streptococcus (anginosus|australis|bovis|constellatus|cristatus|equinus|gallolyticus|gordonii|infantarius|infantis|intermedius|mitis|mutans|oligofermentans|oralis|parasanguinis|peroris|pseudopneumoniae|salivarius|sanguinis|sinensis|sobrinus|thermophilus|vestibularis|viridans)$	AMP	I	AMX, AMC, PIP, TZP	I	Viridans group streptococci	Breakpoints	11	
+genus_species	like	^Streptococcus (anginosus|australis|bovis|constellatus|cristatus|equinus|gallolyticus|gordonii|infantarius|infantis|intermedius|mitis|mutans|oligofermentans|oralis|parasanguinis|peroris|pseudopneumoniae|salivarius|sanguinis|sinensis|sobrinus|thermophilus|vestibularis|viridans)$	AMP	R	AMX, AMC, PIP, TZP	R	Viridans group streptococci	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMP	S	AMX, PIP	S	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMP	I	AMX, PIP	I	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMP	R	AMX, PIP	R	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	PEN	S	AMP, AMX, AMC, PIP, TZP	S	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMC	S	TZP	S	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMC	I	TZP	I	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	AMC	R	TZP	R	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	NAL	S	CIP, LVX, MFX, OFX	S	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Haemophilus influenzae	TCY	S	DOX, MNO	S	Haemophilus influenzae	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	AMC	S	TZP	S	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	AMC	I	TZP	I	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	AMC	R	TZP	R	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	NAL	S	CIP, LVX, MFX, OFX	S	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	ERY	S	AZM, CLR, RXT	S	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	ERY	I	AZM, CLR, RXT	I	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	ERY	R	AZM, CLR, RXT	R	Moraxella catarrhalis	Breakpoints	11	
+genus_species	is	Moraxella catarrhalis	TCY	S	DOX, MNO	S	Moraxella catarrhalis	Breakpoints	11	
+genus	one_of	Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium	PEN	S	AMP, AMX, PIP, TZP, TIC	S	Anaerobic Gram-positives	Breakpoints	11	
+genus	one_of	Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium	PEN	I	AMP, AMX, PIP, TZP, TIC	I	Anaerobic Gram-positives	Breakpoints	11	
+genus	one_of	Actinomyces, Bifidobacterium, Clostridium, Cutibacterium, Eggerthella, Eubacterium, Lactobacillus, Propionibacterium	PEN	R	AMP, AMX, PIP, TZP, TIC	R	Anaerobic Gram-positives	Breakpoints	11	
+genus	one_of	Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella	PEN	S	AMP, AMX, PIP, TZP, TIC	S	Anaerobic Gram-negatives	Breakpoints	11	
+genus	one_of	Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella	PEN	I	AMP, AMX, PIP, TZP, TIC	I	Anaerobic Gram-negatives	Breakpoints	11	
+genus	one_of	Bacteroides, Bilophila , Fusobacterium, Mobiluncus, Porphyromonas, Prevotella	PEN	R	AMP, AMX, PIP, TZP, TIC	R	Anaerobic Gram-negatives	Breakpoints	11	
+genus_species	is	Pasteurella multocida	PEN	S	AMP, AMX	S	Pasteurella multocida	Breakpoints	11	
+genus_species	is	Pasteurella multocida	PEN	I	AMP, AMX	I	Pasteurella multocida	Breakpoints	11	
+genus_species	is	Pasteurella multocida	PEN	R	AMP, AMX	R	Pasteurella multocida	Breakpoints	11	
+genus_species	is	Campylobacter coli	ERY	S	AZM, CLR	S	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter coli	ERY	I	AZM, CLR	I	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter coli	ERY	R	AZM, CLR	R	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter coli	TCY	S	DOX	S	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter coli	TCY	I	DOX	I	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter coli	TCY	R	DOX	R	Campylobacter coli	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	ERY	S	AZM, CLR	S	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	ERY	I	AZM, CLR	I	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	ERY	R	AZM, CLR	R	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	TCY	S	DOX	S	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	TCY	I	DOX	I	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Campylobacter jejuni	TCY	R	DOX	R	Campylobacter jejuni	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	NOR	S	fluoroquinolones	S	Aerococcus sanguinicola	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	NOR	I	fluoroquinolones	I	Aerococcus sanguinicola	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	NOR	R	fluoroquinolones	R	Aerococcus sanguinicola	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	CIP	S	LVX	S	Aerococcus sanguinicola	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	CIP	I	LVX	I	Aerococcus sanguinicola	Breakpoints	11	
+genus_species	is	Aerococcus sanguinicola	CIP	R	LVX	R	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	NOR	S	fluoroquinolones	S	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	NOR	I	fluoroquinolones	I	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	NOR	R	fluoroquinolones	R	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	CIP	S	LVX	S	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	CIP	I	LVX	I	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Aerococcus urinae	CIP	R	LVX	R	Aerococcus urinae	Breakpoints	11	
+genus_species	is	Kingella kingae	PEN	S	AMP, AMX	S	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	PEN	I	AMP, AMX	I	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	PEN	R	AMP, AMX	R	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	ERY	S	AZM, CLR	S	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	ERY	I	AZM, CLR	I	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	ERY	R	AZM, CLR	R	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	TCY	S	DOX	S	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	TCY	I	DOX	I	Kingella kingae	Breakpoints	11	
+genus_species	is	Kingella kingae	TCY	R	DOX	R	Kingella kingae	Breakpoints	11	
+genus_species	is	Burkholderia pseudomallei	TCY	S	DOX	S	Burkholderia pseudomallei	Breakpoints	11	
+genus_species	is	Burkholderia pseudomallei	TCY	I	DOX	I	Burkholderia pseudomallei	Breakpoints	11	
+genus_species	is	Burkholderia pseudomallei	TCY	R	DOX	R	Burkholderia pseudomallei	Breakpoints	11	
+genus	is	Bacillus	NOR	S	fluoroquinolones	S	Bacillus	Breakpoints	11	added in 11
+genus	is	Bacillus	NOR	I	fluoroquinolones	I	Bacillus	Breakpoints	11	added in 11
+genus	is	Bacillus	NOR	R	fluoroquinolones	R	Bacillus	Breakpoints	11	added in 11
+order	is	Enterobacterales			PEN, glycopeptides_except_lipo, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 fullname	like	^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)			aminopenicillins, TIC	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 fullname	like	^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)			aminopenicillins, AMC, CZO, FOX	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus_species	is	Enterobacter cloacae			aminopenicillins, AMC, CZO, FOX	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
@@ -123,17 +232,17 @@ genus_species	is	Klebsiella aerogenes			aminopenicillins, AMC, CZO, FOX	R	Table
 genus_species	is	Escherichia hermannii			aminopenicillins, TIC	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus_species	is	Hafnia alvei			aminopenicillins, AMC, CZO, FOX	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus	is	Klebsiella			aminopenicillins, TIC	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Morganella morganii			aminopenicillins, AMC, CZO, tetracyclines, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Proteus mirabilis			tetracyclines, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Proteus penneri			aminopenicillins, CZO, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Proteus vulgaris			aminopenicillins, CZO, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Providencia rettgeri			aminopenicillins, AMC, CZO, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus_species	is	Providencia stuartii			aminopenicillins, AMC, CZO, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Morganella morganii			aminopenicillins, AMC, CZO, DOX, MNO, TCY, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Proteus mirabilis			DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Proteus penneri			aminopenicillins, CZO, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Proteus vulgaris			aminopenicillins, CZO, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Providencia rettgeri			aminopenicillins, AMC, CZO, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
+genus_species	is	Providencia stuartii			aminopenicillins, AMC, CZO, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus	is	Raoultella			aminopenicillins, TIC	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus_species	is	Serratia marcescens			aminopenicillins, AMC, CZO, FOX, CXM, DOX, TCY, polymyxins, NIT	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus_species	is	Yersinia enterocolitica			aminopenicillins, AMC, TIC, CZO, FOX	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
 genus_species	is	Yersinia pseudotuberculosis			PLB, COL	R	Table 01: Intrinsic resistance in Enterobacterales (at the time: Enterobacteriaceae)	Expert Rules	3.1	
-genus	one_of	Achromobacter, Acinetobacter, Alcaligenes, Bordetella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas			PEN, FOX, CXM, glycopeptides, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
+genus	one_of	Achromobacter, Acinetobacter, Alcaligenes, Bordetella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas			PEN, FOX, CXM, glycopeptides_except_lipo, FUS, macrolides, LIN, streptogramins, RIF, DAP, LNZ	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Acinetobacter baumannii			aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Acinetobacter pittii			aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Acinetobacter nosocomialis			aminopenicillins, AMC, CZO, CTX, CRO, ATM, ETP, TMP, FOS, DOX, TCY	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
@@ -141,10 +250,10 @@ genus_species	is	Acinetobacter calcoaceticus			aminopenicillins, AMC, CZO, CTX,
 genus_species	is	Achromobacter xylosoxidans			aminopenicillins, CZO, CTX, CRO, ETP	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 fullname	like	^Burkholderia (ambifaria|anthina|arboris|cepacia|cenocepacia|contaminans|diffusa|dolosa|lata|latens|metallica|multivorans|paludis|pseudomultivorans|pyrrocinia|pseudomultivorans|seminalis|stabilis|stagnalis|territorii|ubonensis|vietnamiensis)			aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, ATM, ETP, CIP, CHL, aminoglycosides, TMP, FOS, polymyxins	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Elizabethkingia meningoseptica			aminopenicillins, AMC, TIC, CZO, CTX, CRO, CAZ, FEP, ATM, ETP, IPM, MEM, polymyxins	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
-genus_species	is	Ochrobactrum anthropi			aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
-genus_species	is	Pseudomonas aeruginosa			aminopenicillins, AMC, CZO, CTX, CRO, ETP, CHL, KAN, NEO, TMP, SXT, tetracyclines, TGC	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
+genus_species	is	Brucella anthropi			aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
+genus_species	is	Pseudomonas aeruginosa			aminopenicillins, AMC, CZO, CTX, CRO, ETP, CHL, KAN, NEO, TMP, SXT, DOX, MNO, TCY, TGC	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Stenotrophomonas maltophilia			aminopenicillins, AMC, TIC, PIP, TZP, CZO, CTX, CRO, ATM, ETP, IPM, MEM, aminoglycosides, TMP, FOS, TCY	R	Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria	Expert Rules	3.1	
-genus	one_of	Haemophilus, Moraxella, Neisseria, Campylobacter			glycopeptides, LIN, DAP, LNZ	R	Table 03: Intrinsic resistance in other Gram-negative bacteria	Expert Rules	3.1	
+genus	one_of	Haemophilus, Moraxella, Neisseria, Campylobacter			glycopeptides_except_lipo, LIN, DAP, LNZ	R	Table 03: Intrinsic resistance in other Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Haemophilus influenzae			FUS, streptogramins	R	Table 03: Intrinsic resistance in other Gram-negative bacteria	Expert Rules	3.1	
 genus_species	is	Moraxella catarrhalis			TMP	R	Table 03: Intrinsic resistance in other Gram-negative bacteria	Expert Rules	3.1	
 genus	is	Neisseria			TMP	R	Table 03: Intrinsic resistance in other Gram-negative bacteria	Expert Rules	3.1	
@@ -170,8 +279,8 @@ genus_species	is	Enterococcus casseliflavus			FUS, CAZ, cephalosporins_except_CA
 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	3.1	
 genus	is	Corynebacterium			FOS	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
 genus_species	is	Listeria monocytogenes			cephalosporins	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
-genus	one_of	Leuconostoc, Pediococcus			glycopeptides	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
-genus	is	Lactobacillus			glycopeptides	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
+genus	one_of	Leuconostoc, Pediococcus			glycopeptides_except_lipo	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
+genus	is	Lactobacillus			glycopeptides_except_lipo	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
 genus_species	is	Clostridium ramosum			VAN	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
 genus_species	is	Clostridium innocuum			VAN	R	Table 04: Intrinsic resistance in Gram-positive bacteria	Expert Rules	3.1	
 genus_species	one_of	Streptococcus group A, Streptococcus group B, Streptococcus group C, Streptococcus group G	PEN	S	aminopenicillins, cephalosporins_except_CAZ, carbapenems	S	Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci	Expert Rules	3.1	
@@ -189,7 +298,7 @@ genus	is	Staphylococcus	MFX	R	fluoroquinolones	R	Table 13: Interpretive rules fo
 genus_species	is	Streptococcus pneumoniae	MFX	R	fluoroquinolones	R	Table 13: Interpretive rules for quinolones	Expert Rules	3.1	
 order	is	Enterobacterales	CIP	R	fluoroquinolones	R	Table 13: Interpretive rules for quinolones	Expert Rules	3.1	
 genus_species	is	Neisseria gonorrhoeae	CIP	R	fluoroquinolones	R	Table 13: Interpretive rules for quinolones	Expert Rules	3.1	
-order	is	Enterobacterales			PEN, glycopeptides, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+order	is	Enterobacterales			PEN, glycopeptides_except_lipo, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 fullname	like	^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)			aminopenicillins, TIC	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 fullname	like	^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Enterobacter cloacae			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
@@ -199,13 +308,13 @@ genus_species	is	Klebsiella aerogenes			aminopenicillins, AMC, SAM, CZO, CEP, LE
 genus_species	is	Klebsiella oxytoca			aminopenicillins, TIC	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 fullname	like	^Klebsiella( pneumoniae| quasipneumoniae| variicola)?			aminopenicillins, TIC	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Leclercia adecarboxylata			FOS	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Morganella morganii			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, tetracyclines, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Morganella morganii			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, DOX, MNO, TCY, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Plesiomonas shigelloides			aminopenicillins, AMC, SAM	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Proteus mirabilis			tetracyclines, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Proteus penneri			aminopenicillins, CZO, CEP, LEX, CFR, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Proteus vulgaris			aminopenicillins, CZO, CEP, LEX, CFR, CXM, tetracyclines, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Providencia rettgeri			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, tetracyclines, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus_species	is	Providencia stuartii			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, tetracyclines, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Proteus mirabilis			DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Proteus penneri			aminopenicillins, CZO, CEP, LEX, CFR, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Proteus vulgaris			aminopenicillins, CZO, CEP, LEX, CFR, CXM, DOX, MNO, TCY, TGC, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Providencia rettgeri			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, DOX, MNO, TCY, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
+genus_species	is	Providencia stuartii			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, DOX, MNO, TCY, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus	is	Raoultella			aminopenicillins, TIC	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Serratia marcescens			aminopenicillins, AMC, SAM, CZO, CEP, LEX, CFR, FOX, CXM, DOX, TCY, polymyxins, NIT	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Yersinia enterocolitica			aminopenicillins, AMC, SAM, TIC, CZO, CEP, LEX, CFR, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
@@ -215,20 +324,20 @@ genus_species	is	Aeromonas veronii			aminopenicillins, AMC, SAM, FOX	R	Table 1:
 genus_species	is	Aeromonas dhakensis			aminopenicillins, AMC, SAM, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Aeromonas caviae			aminopenicillins, AMC, SAM, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
 genus_species	is	Aeromonas jandaei			aminopenicillins, AMC, SAM, TIC,  CZO, CEP, LEX, CFR, FOX	R	Table 1: Intrinsic resistance in Enterobacterales and Aeromonas spp.	Expert Rules	3.2	
-genus	one_of	Achromobacter, Acinetobacter, Alcaligenes, Bordetella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas			PEN, cephalosporins_1st, cephalosporins_2nd, glycopeptides, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	
+genus	one_of	Achromobacter, Acinetobacter, Alcaligenes, Bordetella, Burkholderia, Elizabethkingia, Flavobacterium, Ochrobactrum, Pseudomonas, Stenotrophomonas			PEN, cephalosporins_1st, cephalosporins_2nd, glycopeptides_except_lipo, lipoglycopeptides, FUS, macrolides, lincosamides, streptogramins, RIF, oxazolidinones	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	
 fullname	like	^Acinetobacter (baumannii|pittii|nosocomialis)			aminopenicillins, AMC, CRO, CTX, ATM, ETP, TMP, FOS, DOX, TCY	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
 genus	is	Acinetobacter			DOX, TCY	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
 genus_species	is	Achromobacter xylosoxidans 			aminopenicillins, CRO, CTX, ETP	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
 fullname	like	^Burkholderia (ambifaria|anthina|arboris|cepacia|cenocepacia|contaminans|diffusa|dolosa|lata|latens|metallica|multivorans|paludis|pseudomultivorans|pyrrocinia|pseudomultivorans|seminalis|stabilis|stagnalis|territorii|ubonensis|vietnamiensis)			aminopenicillins, AMC, SAM, TIC, TCC, PIP, TZP, CRO, CTX, ATM, ETP, CIP, CHL, aminoglycosides, TMP, FOS, polymyxins	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
 genus_species	is	Elizabethkingia meningoseptica			aminopenicillins, AMC, SAM, TIC, TCC, PIP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP, IPM, MEM, polymyxins	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
-genus_species	is	Ochrobactrum anthropi			aminopenicillins, AMC, SAM, TIC, TCC, PIP, TZP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
-genus_species	is	Pseudomonas aeruginosa			aminopenicillins, AMC, SAM, CTX, CRO, ETP, CHL, KAN, NEO, TMP, tetracyclines, TGC	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
+genus_species	is	Brucella anthropi			aminopenicillins, AMC, SAM, TIC, TCC, PIP, TZP, CZO, CTX, CRO, CAZ, FEP, ATM, ETP	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
+genus_species	is	Pseudomonas aeruginosa			aminopenicillins, AMC, SAM, CTX, CRO, ETP, CHL, KAN, NEO, TMP, DOX, MNO, TCY, TGC	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
 genus_species	is	Stenotrophomonas maltophilia			aminopenicillins, AMC, SAM, TIC, PIP, TZP, CRO, CTX, ATM, ETP, IPM, MEM, aminoglycosides, TMP, FOS, TCY	R	Table 2: Intrinsic resistance in non-fermentative gram-negative bacteria	Expert Rules	3.2	Additional rules from header added in separate rule (genus is one of…)
-genus_species	is	Haemophilus influenzae			FUS, streptogramins, glycopeptides, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
-genus_species	is	Moraxella catarrhalis			TMP, glycopeptides, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
-genus	is	Neisseria			TMP, glycopeptides, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
-genus_species	is	Campylobacter fetus			FUS, streptogramins, TMP, NAL, glycopeptides, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
-fullname	like	^Campylobacter (jejuni|coli)			FUS, streptogramins, TMP, glycopeptides, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
+genus_species	is	Haemophilus influenzae			FUS, streptogramins, glycopeptides_except_lipo, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
+genus_species	is	Moraxella catarrhalis			TMP, glycopeptides_except_lipo, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
+genus	is	Neisseria			TMP, glycopeptides_except_lipo, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
+genus_species	is	Campylobacter fetus			FUS, streptogramins, TMP, NAL, glycopeptides_except_lipo, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
+fullname	like	^Campylobacter (jejuni|coli)			FUS, streptogramins, TMP, glycopeptides_except_lipo, lipoglycopeptides, lincosamides, oxazolidinones	R	Table 3: Intrinsic resistance in other gram-negative bacteria	Expert Rules	3.2	
 gramstain	is	Gram-positive			ATM, TEM, polymyxins, NAL	R	Table 4: Intrinsic resistance in gram-positive bacteria	Expert Rules	3.2	
 genus_species	is	Staphylococcus saprophyticus			FUS, CAZ, FOS, NOV	R	Table 4: Intrinsic resistance in gram-positive bacteria	Expert Rules	3.2	
 genus_species	is	Staphylococcus cohnii			CAZ, NOV	R	Table 4: Intrinsic resistance in gram-positive bacteria	Expert Rules	3.2	
@@ -263,8 +372,8 @@ fullname	like	^(Serratia|Providencia|Morganella morganii)			TGC	R	Expert Rules o
 genus	is	Salmonella			cephalosporins_2nd	R	Expert Rules on Salmonella	Expert Rules	3.2	
 genus	is	Salmonella			aminoglycosides	R	Expert Rules on Salmonella	Expert Rules	3.2	
 genus	is	Salmonella	PEF	R	CIP	R	Expert Rules on Salmonella	Expert Rules	3.2	
-genus_species	is	Staphylococcus aureus	FOX1	R	all_betalactams	R	Expert Rules on Staphylococcus	Expert Rules	3.2	
-genus_species	is	Staphylococcus aureus	FOX1	S	all_betalactams	S	Expert Rules on Staphylococcus	Expert Rules	3.2	
+genus_species	is	Staphylococcus aureus	FOX1	R	betalactams	R	Expert Rules on Staphylococcus	Expert Rules	3.2	
+genus_species	is	Staphylococcus aureus	FOX1	S	betalactams	S	Expert Rules on Staphylococcus	Expert Rules	3.2	
 genus_species	one_of	Staphylococcus aureus, Staphylococcus lugdunensis	PEN	R	AMP, AMX, AZL, BAM, CRB, CRN, EPC, HET, MEC, MEZ, MTM, PIP, PME, PVM, SBC, TAL, TEM, TIC	R	Expert Rules on Staphylococcus	Expert Rules	3.2	all penicillins without beta-lactamse inhibitor
 genus	is	Staphylococcus	ERY, CLI	S	macrolides, lincosamides	S	Expert Rules on Staphylococcus	Expert Rules	3.2	
 genus	is	Staphylococcus	NOR	S	CIP, LVX, MFX, OFX	S	Expert Rules on Staphylococcus	Expert Rules	3.2	
@@ -291,7 +400,7 @@ genus_species	is	Streptococcus pneumoniae	TCY	S	DOX, MNO	S	Expert Rules on Strep
 genus_species	is	Streptococcus pneumoniae	TCY	R	DOX, MNO	R	Expert Rules on Streptococcus pneumoniae	Expert Rules	3.2	
 genus_species	is	Streptococcus pneumoniae	VAN	S	lipoglycopeptides	S	Expert Rules on Streptococcus pneumoniae	Expert Rules	3.2	
 fullname	like	^Streptococcus (anginosus|australis|bovis|constellatus|cristatus|equinus|gallolyticus|gordonii|infantarius|infantis|intermedius|mitis|mutans|oligofermentans|oralis|parasanguinis|peroris|pseudopneumoniae|salivarius|sanguinis|sinensis|sobrinus|thermophilus|vestibularis|viridans)$	PEN	S	aminopenicillins, CTX, CRO	S	Expert Rules on Viridans Group Streptococci	Expert Rules	3.2	
-genus_species	is	Haemophilus influenzae 	PEN	S	all_betalactams	S	Expert Rules on Haemophilus influenzae	Expert Rules	3.2	
+genus_species	is	Haemophilus influenzae 	PEN	S	betalactams	S	Expert Rules on Haemophilus influenzae	Expert Rules	3.2	
 genus_species	is	Haemophilus influenzae 	NAL	S	fluoroquinolones	S	Expert Rules on Haemophilus influenzae	Expert Rules	3.2	
 genus_species	is	Haemophilus influenzae 	NAL	R	CIP, LVX, MFX	R	Expert Rules on Haemophilus influenzae	Expert Rules	3.2	
 genus_species	is	Haemophilus influenzae 	TCY	S	DOX, MNO	S	Expert Rules on Haemophilus influenzae	Expert Rules	3.2	
@@ -300,3 +409,9 @@ genus_species	is	Moraxella catarrhalis	NAL	S	fluoroquinolones	S	Expert Rules on
 genus_species	is	Moraxella catarrhalis	NAL	R	fluoroquinolones	R	Expert Rules on Moraxella catarrhalis	Expert Rules	3.2	
 genus	is	Campylobacter	ERY	S	CLR, AZM	S	Expert Rules on Campylobacter	Expert Rules	3.2	
 genus_species	is	Campylobacter	ERY	R	CLR, AZM	R	Expert Rules on Campylobacter	Expert Rules	3.2	
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CTX	S	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CRO	S	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CAZ	S	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CTX	I	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CRO	I	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
+fullname	like	^(Enterobacter|Klebsiella aerogenes|Citrobacter braakii|Citrobacter freundii|Citrobacter gillenii|Citrobacter murliniae|Citrobacter rodenticum|Citrobacter sedlakii|Citrobacter werkmanii|Citrobacter youngae|Hafnia alvei|Serratia|Morganella morganii|Providencia)	CAZ	I	CTX, CRO, CAZ		Expert Rules on Enterobacterales (AmpC de-repressed cephalosporins)	Expert Rules	3.2	This is rule 3 and 4 of EUCAST Expert Rules v3.2 on Enterobacterales, result will be set with the 'ampc_derepressed_cephalosporins' argument
--- a/data-raw/internals.R
+++ b/data-raw/internals.R
@@ -1,163 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
-#                                                                      #
-# SOURCE                                                               #
-# https://github.com/msberends/AMR                                     #
-#                                                                      #
-# LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
-# Developed at the University of Groningen, the Netherlands, in        #
-# collaboration with non-profit organisations Certe Medical            #
-# Diagnostics & Advice, and University Medical Center Groningen.       # 
-#                                                                      #
-# This R package is free software; you can freely use and distribute   #
-# it for both personal and commercial purposes under the terms of the  #
-# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
-# the Free Software Foundation.                                        #
-# We created this package for both routine data analysis and academic  #
-# research and it was publicly released in the hope that it will be    #
-# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
-#                                                                      #
-# Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
-# ==================================================================== #
-
-# Run this file to update the package using: -------------------------------
-# source("data-raw/internals.R")
-# --------------------------------------------------------------------------
-
-# See 'data-raw/eucast_rules.tsv' for the EUCAST reference file
-library(dplyr, warn.conflicts = FALSE)
-eucast_rules_file <- utils::read.delim(file = "data-raw/eucast_rules.tsv",
-                                       skip = 10,
-                                       sep = "\t",
-                                       stringsAsFactors = FALSE,
-                                       header = TRUE,
-                                       strip.white = TRUE,
-                                       na = c(NA, "", NULL)) %>% 
-  # take the order of the reference.rule_group column in the original data file
-  mutate(reference.rule_group = factor(reference.rule_group,
-                                       levels = unique(reference.rule_group),
-                                       ordered = TRUE),
-         sorting_rule = ifelse(grepl("^Table", reference.rule, ignore.case = TRUE), 1, 2)) %>% 
-  arrange(reference.rule_group,
-          reference.version,
-          sorting_rule,
-          reference.rule) %>% 
-  mutate(reference.rule_group = as.character(reference.rule_group)) %>% 
-  select(-sorting_rule)
-
-# Translations ----
-translations_file <- utils::read.delim(file = "data-raw/translations.tsv",
-                                       sep = "\t",
-                                       stringsAsFactors = FALSE,
-                                       header = TRUE,
-                                       blank.lines.skip = TRUE,
-                                       fill = TRUE,
-                                       strip.white = TRUE,
-                                       encoding = "UTF-8",
-                                       fileEncoding = "UTF-8",
-                                       na.strings = c(NA, "", NULL),
-                                       allowEscapes = TRUE, # else "\\1" will be imported as "\\\\1"
-                                       quote = "")
-
-# Old microorganism codes -------------------------------------------------
-
-microorganisms.translation <- readRDS("data-raw/microorganisms.translation.rds")
-
-# Export to package as internal data ----
-usethis::use_data(eucast_rules_file, translations_file, microorganisms.translation,
-                  internal = TRUE,
-                  overwrite = TRUE,
-                  version = 2,
-                  compress = "xz")
-
-# Remove from global environment ----
-rm(eucast_rules_file)
-rm(translations_file)
-rm(microorganisms.translation)
-
-# Save to raw data to repository ----
-write_md5 <- function(object) {
-  conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
-  writeLines(digest::digest(object, "md5"), conn)
-  close(conn)
-}
-changed_md5 <- function(object) {
-  tryCatch({
-    conn <- file(paste0("data-raw/", deparse(substitute(object)), ".md5"))
-    compared <- digest::digest(object, "md5") != readLines(con = conn)
-    close(conn)
-    compared
-  }, error = function(e) TRUE)
-}
-usethis::ui_done(paste0("Saving raw data to {usethis::ui_value('/data-raw/')}"))
-devtools::load_all(quiet = TRUE)
-# give official names to ABs and MOs
-rsi <- dplyr::mutate(rsi_translation, ab = ab_name(ab), mo = mo_name(mo))
-if (changed_md5(rsi)) {
-  write_md5(rsi)
-  try(saveRDS(rsi, "data-raw/rsi_translation.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(rsi, "data-raw/rsi_translation.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(rsi, "data-raw/rsi_translation.sas"), silent = TRUE)
-  try(haven::write_sav(rsi, "data-raw/rsi_translation.sav"), silent = TRUE)
-  try(haven::write_dta(rsi, "data-raw/rsi_translation.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(rsi, "data-raw/rsi_translation.xlsx"), silent = TRUE)
-}
-
-mo <- dplyr::mutate_if(microorganisms, ~!is.numeric(.), as.character)
-if (changed_md5(mo)) {
-  write_md5(mo)
-  try(saveRDS(mo, "data-raw/microorganisms.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(mo, "data-raw/microorganisms.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(mo, "data-raw/microorganisms.sas"), silent = TRUE)
-  try(haven::write_sav(mo, "data-raw/microorganisms.sav"), silent = TRUE)
-  try(haven::write_dta(mo, "data-raw/microorganisms.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(mo, "data-raw/microorganisms.xlsx"), silent = TRUE)
-}
-
-if (changed_md5(microorganisms.old)) {
-  write_md5(microorganisms.old)
-  try(saveRDS(microorganisms.old, "data-raw/microorganisms.old.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(microorganisms.old, "data-raw/microorganisms.old.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(microorganisms.old, "data-raw/microorganisms.old.sas"), silent = TRUE)
-  try(haven::write_sav(microorganisms.old, "data-raw/microorganisms.old.sav"), silent = TRUE)
-  try(haven::write_dta(microorganisms.old, "data-raw/microorganisms.old.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(microorganisms.old, "data-raw/microorganisms.old.xlsx"), silent = TRUE)
-}
-
-ab <- dplyr::mutate_if(antibiotics, ~!is.numeric(.), as.character)
-if (changed_md5(ab)) {
-  write_md5(ab)
-  try(saveRDS(ab, "data-raw/antibiotics.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(ab, "data-raw/antibiotics.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(ab, "data-raw/antibiotics.sas"), silent = TRUE)
-  try(haven::write_sav(ab, "data-raw/antibiotics.sav"), silent = TRUE)
-  try(haven::write_dta(ab, "data-raw/antibiotics.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(ab, "data-raw/antibiotics.xlsx"), silent = TRUE)
-}
-
-av <- dplyr::mutate_if(antivirals, ~!is.numeric(.), as.character)
-if (changed_md5(av)) {
-  write_md5(av)
-  try(saveRDS(av, "data-raw/antivirals.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(av, "data-raw/antivirals.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(av, "data-raw/antivirals.sas"), silent = TRUE)
-  try(haven::write_sav(av, "data-raw/antivirals.sav"), silent = TRUE)
-  try(haven::write_dta(av, "data-raw/antivirals.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(av, "data-raw/antivirals.xlsx"), silent = TRUE)
-}
-
-if (changed_md5(intrinsic_resistant)) {
-  write_md5(intrinsic_resistant)
-  try(saveRDS(intrinsic_resistant, "data-raw/intrinsic_resistant.rds", version = 2, compress = "xz"), silent = TRUE)
-  try(write.table(intrinsic_resistant, "data-raw/intrinsic_resistant.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
-  try(haven::write_sas(intrinsic_resistant, "data-raw/intrinsic_resistant.sas"), silent = TRUE)
-  try(haven::write_sav(intrinsic_resistant, "data-raw/intrinsic_resistant.sav"), silent = TRUE)
-  try(haven::write_dta(intrinsic_resistant, "data-raw/intrinsic_resistant.dta"), silent = TRUE)
-  try(openxlsx::write.xlsx(intrinsic_resistant, "data-raw/intrinsic_resistant.xlsx"), silent = TRUE)
-}
-
-rm(write_md5)
-rm(changed_md5)
--- a/data-raw/intrinsic_resistant.dta
+++ b/data-raw/intrinsic_resistant.dta
--- a/data-raw/intrinsic_resistant.md5
+++ b/data-raw/intrinsic_resistant.md5
@@ -1 +1 @@
-b6de75043ef27eabd6fff22f04638225
+9c58b2d894dbad7593cd44b78d04cd78
--- a/data-raw/intrinsic_resistant.rds
+++ b/data-raw/intrinsic_resistant.rds
--- a/data-raw/intrinsic_resistant.sas
+++ b/data-raw/intrinsic_resistant.sas
--- a/data-raw/intrinsic_resistant.sav
+++ b/data-raw/intrinsic_resistant.sav
--- a/data-raw/intrinsic_resistant.txt
+++ b/data-raw/intrinsic_resistant.txt
--- a/data-raw/intrinsic_resistant.xlsx
+++ b/data-raw/intrinsic_resistant.xlsx
--- a/data-raw/loinc.R
+++ b/data-raw/loinc.R
@@ -1,12 +1,12 @@
 # ==================================================================== #
 # TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis for R                        #
+# Antimicrobial Resistance (AMR) Data Analysis for R                   #
 #                                                                      #
 # SOURCE                                                               #
 # https://github.com/msberends/AMR                                     #
 #                                                                      #
 # LICENCE                                                              #
-# (c) 2018-2020 Berends MS, Luz CF et al.                              #
+# (c) 2018-2021 Berends MS, Luz CF et al.                              #
 # Developed at the University of Groningen, the Netherlands, in        #
 # collaboration with non-profit organisations Certe Medical            #
 # Diagnostics & Advice, and University Medical Center Groningen.       # 
@@ -20,7 +20,7 @@
 # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
 #                                                                      #
 # Visit our website for the full manual and a complete tutorial about  #
-# how to conduct AMR analysis: https://msberends.github.io/AMR/        #
+# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #

 # last updated: 20 January 2020 - Loinc_2.67
--- a/data-raw/microorganisms.dta
+++ b/data-raw/microorganisms.dta
--- a/data-raw/microorganisms.old.dta
+++ b/data-raw/microorganisms.old.dta
--- a/data-raw/microorganisms.old.md5
+++ b/data-raw/microorganisms.old.md5
@@ -1 +1 @@
-617b59b8ac3bd1aad7847aafc328f0f3
+8338ff5f079f4519fa3c44f8c5bace64
--- a/data-raw/microorganisms.old.rds
+++ b/data-raw/microorganisms.old.rds
--- a/data-raw/microorganisms.old.sas
+++ b/data-raw/microorganisms.old.sas
--- a/data-raw/microorganisms.old.sav
+++ b/data-raw/microorganisms.old.sav
--- a/data-raw/microorganisms.old.txt
+++ b/data-raw/microorganisms.old.txt
--- a/data-raw/microorganisms.old.xlsx
+++ b/data-raw/microorganisms.old.xlsx
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
dr. M.S. (Matthijs) Berends	ac73a8d849	v1.7.0	2021-05-24 15:29:17 +02:00
dr. M.S. (Matthijs) Berends	e5599bc694	(v1.6.0.9065) unit tests	2021-05-24 11:01:32 +02:00
dr. M.S. (Matthijs) Berends	4fbf9e1720	(v1.6.0.9064) prepare new release	2021-05-24 09:34:08 +02:00
dr. M.S. (Matthijs) Berends	a13fd98e8b	(v1.6.0.9063) prepare new release	2021-05-24 09:00:11 +02:00
dr. M.S. (Matthijs) Berends	06302d296a	(v1.6.0.9062) code consistency	2021-05-24 00:06:28 +02:00
dr. M.S. (Matthijs) Berends	07939b1a14	(v1.6.0.9061) age() update	2021-05-23 23:11:16 +02:00
dr. M.S. (Matthijs) Berends	fa2f5214b9	(v1.6.0.9060) unit tests	2021-05-22 10:05:59 +02:00
dr. M.S. (Matthijs) Berends	adca43f8d9	covr update	2021-05-22 09:22:39 +02:00
dr. M.S. (Matthijs) Berends	0b1f59edec	(v1.6.0.9058) unit tests	2021-05-22 08:58:51 +02:00
dr. M.S. (Matthijs) Berends	808024c5f4	covr	2021-05-21 23:13:01 +02:00
dr. M.S. (Matthijs) Berends	65a8b58aa6	(v1.6.0.9056) support codecov again	2021-05-21 20:30:48 +02:00
dr. M.S. (Matthijs) Berends	b210f1327c	(v1.6.0.9055) support codecov again	2021-05-21 20:20:51 +02:00
dr. M.S. (Matthijs) Berends	fecc5d183c	(v1.6.0.9054) unit tests	2021-05-20 15:06:08 +02:00
dr. M.S. (Matthijs) Berends	69a656abc0	(v1.6.0.9053) unit tests	2021-05-20 13:42:17 +02:00
dr. M.S. (Matthijs) Berends	4a2a48b7c1	(v1.6.0.9052) unit tests	2021-05-20 11:42:39 +02:00
dr. M.S. (Matthijs) Berends	d1b1828ab8	unit test	2021-05-20 10:55:07 +02:00
dr. M.S. (Matthijs) Berends	04ef5b28e7	(v1.6.0.9050) printing NA in custom_eucast_rules()	2021-05-20 10:10:40 +02:00
dr. M.S. (Matthijs) Berends	9a2879cba9	(v1.6.0.9049) unit tests	2021-05-20 00:07:27 +02:00
dr. M.S. (Matthijs) Berends	2413efd5c1	(v1.6.0.9048) ab selectors overhaul	2021-05-19 22:55:42 +02:00
dr. M.S. (Matthijs) Berends	6920c0be41	(v1.6.0.9047) filter_ab_class() fixes	2021-05-18 11:29:31 +02:00
dr. M.S. (Matthijs) Berends	7028dcfa5b	that 1 AM error	2021-05-18 01:05:44 +02:00
dr. M.S. (Matthijs) Berends	d67371acd1	that 1 AM error	2021-05-18 00:58:39 +02:00
dr. M.S. (Matthijs) Berends	cfb7df823e	(v1.6.0.9044) betalactams() selector	2021-05-18 00:53:04 +02:00
dr. M.S. (Matthijs) Berends	be49131ed7	(v1.6.0.9043) translation update	2021-05-17 19:43:01 +02:00
dr. M.S. (Matthijs) Berends	83fec69a03	(v1.6.0.9042) translation update	2021-05-17 11:26:12 +02:00
dr. M.S. (Matthijs) Berends	916df6e90c	(v1.6.0.9041) filter_ab_class() fix	2021-05-16 10:50:00 +02:00
dr. M.S. (Matthijs) Berends	00496e45b7	(v1.6.0.9040) unit tests	2021-05-16 09:25:36 +02:00
dr. M.S. (Matthijs) Berends	6c3ab19e3a	(v1.6.0.9038) unit tests	2021-05-15 23:47:36 +02:00
dr. M.S. (Matthijs) Berends	3619c1327c	(v1.6.0.9037) unit tests	2021-05-15 23:36:02 +02:00
dr. M.S. (Matthijs) Berends	73fb0374c3	(v1.6.0.9036) unit tests	2021-05-15 23:25:10 +02:00
dr. M.S. (Matthijs) Berends	229e1bb407	(v1.6.0.9035) unit tests	2021-05-15 22:55:12 +02:00
dr. M.S. (Matthijs) Berends	6e60ddf8d7	(v1.6.0.9034) unit tests	2021-05-15 22:35:57 +02:00
dr. M.S. (Matthijs) Berends	54dd868b22	(v1.6.0.9034) unit tests	2021-05-15 22:30:11 +02:00
dr. M.S. (Matthijs) Berends	0ce9fb4da2	(v1.6.0.9033) unit tests	2021-05-15 22:11:36 +02:00
dr. M.S. (Matthijs) Berends	86736ab9a7	(v1.6.0.9032) unit tests	2021-05-15 21:54:56 +02:00
dr. M.S. (Matthijs) Berends	d8c91d5876	(v1.6.0.9031) tinytest unit tests	2021-05-15 21:36:22 +02:00
dr. M.S. (Matthijs) Berends	9a381c8d18	(v1.6.0.9030) new unit test flow	2021-05-13 23:07:31 +02:00
dr. M.S. (Matthijs) Berends	c17acbe712	unit test fix	2021-05-13 22:44:59 +02:00
dr. M.S. (Matthijs) Berends	9ed2f6490f	(v1.6.0.9028) new unit test flow	2021-05-13 22:44:11 +02:00
dr. M.S. (Matthijs) Berends	5b9fb8daf4	(v1.6.0.9027) new unit test flow	2021-05-13 21:54:15 +02:00
dr. M.S. (Matthijs) Berends	b1d942be91	(v1.6.0.9026) new unit test flow	2021-05-13 21:16:22 +02:00
dr. M.S. (Matthijs) Berends	994d157aa6	(v1.6.0.9025) unit test update	2021-05-13 20:53:56 +02:00
dr. M.S. (Matthijs) Berends	9d9d62eba4	(v1.6.0.9024) unit test update	2021-05-13 20:49:47 +02:00
dr. M.S. (Matthijs) Berends	aeea00881e	(v1.6.0.9023) new unit test flow	2021-05-13 19:31:47 +02:00
dr. M.S. (Matthijs) Berends	655b813e99	(v1.6.0.9022) unit test fix	2021-05-13 15:56:12 +02:00
dr. M.S. (Matthijs) Berends	29dbfa2f49	(v1.6.0.9021) join functions update	2021-05-12 18:15:03 +02:00
dr. M.S. (Matthijs) Berends	3319fbae58	(v1.6.0.9020) fix for skimr in dplyr 1.0.6	2021-05-06 15:17:11 +02:00
dr. M.S. (Matthijs) Berends	5899678b74	(v1.6.0.9019) website fix	2021-05-05 15:47:39 +02:00
dr. M.S. (Matthijs) Berends	0aca719929	(v1.6.0.9018) unit tests	2021-05-04 15:20:43 +02:00
dr. M.S. (Matthijs) Berends	5679ccdaf9	(v1.6.0.9017) extra system codes	2021-05-04 12:47:33 +02:00
dr. M.S. (Matthijs) Berends	f33e61bac7	(v1.6.0.9016) website update and c() fixes	2021-05-03 13:06:43 +02:00
dr. M.S. (Matthijs) Berends	12a8d59869	(v1.6.0.9015) italicise_taxonomy	2021-05-03 10:47:32 +02:00
dr. M.S. (Matthijs) Berends	e405de079c	(v1.6.0.9014) as.rsi() for numeric values	2021-04-30 13:18:48 +02:00
dr. M.S. (Matthijs) Berends	a9fd4aa49f	(v1.6.0.9013) website update	2021-04-29 17:16:30 +02:00
dr. M.S. (Matthijs) Berends	5e06b20d43	(v1.6.0.9012) unit tests	2021-04-27 11:28:17 +02:00
dr. M.S. (Matthijs) Berends	c5fff1c95c	(v1.6.0.9011) unit tests	2021-04-27 10:27:13 +02:00
dr. M.S. (Matthijs) Berends	93683a4ae2	(v1.6.0.9010) big first_isolate() update	2021-04-26 23:57:37 +02:00
dr. M.S. (Matthijs) Berends	5f9e7bd3ee	(v1.6.0.9009) key_antibiotics update	2021-04-23 16:13:26 +02:00
dr. M.S. (Matthijs) Berends	70b803dbb6	(v1.6.0.9008) unlike, bugfix for col_mo naming	2021-04-23 09:59:36 +02:00
dr. M.S. (Matthijs) Berends	c6289c3fc3	(v1.6.0.9007) documentation custom eucast rules, progress bar as.mo	2021-04-20 10:46:17 +02:00
dr. M.S. (Matthijs) Berends	de66eccf43	(v1.6.0.9006) eucast rules fix for Ochrobactrum anthropi	2021-04-16 14:59:57 +02:00
dr. M.S. (Matthijs) Berends	24ac18a99d	(v1.6.0.9005) unit test fix	2021-04-16 13:24:59 +02:00
dr. M.S. (Matthijs) Berends	9842ef9660	(v1.6.0.9004) unit test fix	2021-04-16 12:02:57 +02:00
dr. M.S. (Matthijs) Berends	00d3e437a8	(v1.6.0.9003) like() fix	2021-04-16 11:41:05 +02:00
dr. M.S. (Matthijs) Berends	d277d58475	(v1.6.0.9002) R-3.0 installation fix	2021-04-12 14:24:40 +02:00
dr. M.S. (Matthijs) Berends	6ff5448192	(v1.6.0.9001) support Inf for episodes	2021-04-12 12:35:13 +02:00
dr. M.S. (Matthijs) Berends	7a3139f7cc	(v1.6.0.9000) custom EUCAST rules	2021-04-07 08:37:42 +02:00
dr. M.S. (Matthijs) Berends	551f99dc8f	v1.6.0	2021-03-15 07:23:21 +01:00
dr. M.S. (Matthijs) Berends	4e0a9533ad	(v1.5.0.9041) SNOMED update	2021-03-11 21:42:30 +01:00
dr. M.S. (Matthijs) Berends	8d6ceb6a15	(v1.5.0.9040) removal of isolate_identifier	2021-03-08 09:44:17 +01:00
dr. M.S. (Matthijs) Berends	a7c9b4c295	(v1.5.0.9039) handle first isolates for missing antibiograms	2021-03-08 02:38:32 +01:00
dr. M.S. (Matthijs) Berends	461793dc34	(v1.5.0.9038) quick test	2021-03-08 01:24:37 +01:00
dr. M.S. (Matthijs) Berends	a12975bc6e	(v1.5.0.9037) quick test	2021-03-08 00:13:13 +01:00
dr. M.S. (Matthijs) Berends	68163e3089	(v1.5.0.9036) quick test	2021-03-07 22:45:05 +01:00
dr. M.S. (Matthijs) Berends	2f0fc3cab7	(v1.5.0.9035) quick test	2021-03-07 22:15:25 +01:00
dr. M.S. (Matthijs) Berends	450bbe5fb7	(v1.5.0.9034) unit test fix	2021-03-07 21:16:45 +01:00
dr. M.S. (Matthijs) Berends	1bbf409fe5	(v1.5.0.9033) as.rsi fix	2021-03-07 16:15:43 +01:00
dr. M.S. (Matthijs) Berends	850c123de7	(v1.5.0.9032) All group generics for MICs	2021-03-07 13:52:39 +01:00
dr. M.S. (Matthijs) Berends	91dd755cac	(v1.5.0.9031) math processing of MICs	2021-03-05 15:36:39 +01:00
dr. M.S. (Matthijs) Berends	0d29bde693	(v1.5.0.9030) as.ab() speed improvement	2021-03-05 10:32:09 +01:00
dr. M.S. (Matthijs) Berends	0e0e3c4ffa	(v1.5.0.9029) added missing families and orders	2021-03-05 01:31:46 +01:00
dr. M.S. (Matthijs) Berends	ddf88345f1	(v1.5.0.9028) Updated taxonomy until March 2021	2021-03-04 23:28:32 +01:00
dr. M.S. (Matthijs) Berends	41f94cde97	(v1.5.0.9027) website update	2021-02-26 12:11:29 +01:00
dr. M.S. (Matthijs) Berends	1737d56ae4	(v1.5.0.9026) vignette update, support for GISA	2021-02-25 12:31:12 +01:00
dr. M.S. (Matthijs) Berends	a673407904	(v1.5.0.9025) big plot and ggplot generics update	2021-02-25 10:33:08 +01:00
dr. M.S. (Matthijs) Berends	31ceba5441	(v1.5.0.9024) more speed improvements	2021-02-22 20:21:33 +01:00
dr. M.S. (Matthijs) Berends	0fdabff1ba	(v1.5.0.9023) mo properties speed improvement	2021-02-21 23:19:40 +01:00
dr. M.S. (Matthijs) Berends	062c49fca1	(v1.5.0.9022) mo properties speed improvement	2021-02-21 22:56:35 +01:00
dr. M.S. (Matthijs) Berends	5ef8cb41a7	(v1.5.0.9021) improve speed of %like%	2021-02-21 20:15:09 +01:00
dr. M.S. (Matthijs) Berends	daa12ced2c	(v1.5.0.9020) translation fix	2021-02-18 23:23:14 +01:00
dr. M.S. (Matthijs) Berends	601ea7377c	(v1.5.0.9019) use functions without loading AMR pkg	2021-02-17 10:58:13 +01:00
dr. M.S. (Matthijs) Berends	2dd0656355	(v1.5.0.9018) fix unit tests	2021-02-09 12:28:15 +01:00
dr. M.S. (Matthijs) Berends	cb404492b2	(v1.5.0.9017) unit testing	2021-02-08 21:09:36 +01:00
dr. M.S. (Matthijs) Berends	4a84894f79	(v1.5.0.9016) only_rsi_columns update, documentation	2021-02-08 14:18:42 +01:00
dr. M.S. (Matthijs) Berends	8fda473e49	(v1.5.0.9015) unit test fix, grouped first isolates	2021-02-04 16:48:16 +01:00
dr. M.S. (Matthijs) Berends	2eca8c3f01	(v1.5.0.9014) only_rsi_columns, is.rsi.eligible improvement	2021-02-02 23:57:35 +01:00
dr. M.S. (Matthijs) Berends	20d638c193	(v1.5.0.9013) updated tibble printing colours	2021-01-28 16:09:30 +01:00
dr. M.S. (Matthijs) Berends	331c1f6508	(v1.5.0.9012) ampc_cephalosporin_resistance for I	2021-01-25 21:58:00 +01:00
dr. M.S. (Matthijs) Berends	24eb4453db	(v1.5.0.9011) small dosage update	2021-01-24 23:27:11 +01:00
dr. M.S. (Matthijs) Berends	286eaa9699	(v1.5.0.9010) MDRO vignette update, get_episode for < day	2021-01-24 14:48:56 +01:00
dr. M.S. (Matthijs) Berends	1a88caa119	(v1.5.0.9009) unit test fixes	2021-01-22 10:55:07 +01:00
dr. M.S. (Matthijs) Berends	1ba44776a1	(v1.5.0.9008) Internal data sets to pkg, speed for auto col determination	2021-01-22 10:20:41 +01:00
dr. M.S. (Matthijs) Berends	27f084d819	(v1.5.0.9007) updated unit tests	2021-01-18 18:45:43 +01:00
dr. M.S. (Matthijs) Berends	4eab095306	(v1.5.0.9006) major documentation update	2021-01-18 16:57:56 +01:00
dr. M.S. (Matthijs) Berends	e95218c0d1	(v1.5.0.9005) custom MDRO guideline	2021-01-17 10:35:21 +01:00
dr. M.S. (Matthijs) Berends	e699de955c	(v1.5.0.9004) custom MDRO guideline	2021-01-17 00:26:48 +01:00
dr. M.S. (Matthijs) Berends	7ebc534ccd	(v1.5.0.9003) verbose output of mdro()	2021-01-15 22:44:52 +01:00
dr. M.S. (Matthijs) Berends	6745f3fb17	(v1.5.0.9002) doc update	2021-01-14 15:51:41 +01:00
dr. M.S. (Matthijs) Berends	bc00470dca	(v1.5.0.9001) more informative argument errors	2021-01-14 14:41:44 +01:00
dr. M.S. (Matthijs) Berends	d014955ce0	(v.1.5.0.9000) implementation of EUCAST rules v11 (2021)	2021-01-12 22:08:04 +01:00
dr. M.S. (Matthijs) Berends	3b84b8be75	v1.5.0	2021-01-06 11:16:17 +01:00
dr. M.S. (Matthijs) Berends	1563dcd1aa	(v1.4.0.9062) unit test fix	2021-01-04 14:46:17 +01:00
dr. M.S. (Matthijs) Berends	a7ea4c9d2f	(v1.4.0.9061) ab class selector fix	2021-01-04 14:16:09 +01:00
dr. M.S. (Matthijs) Berends	8d117820b8	(v1.4.0.9060) ab class selector fix	2021-01-04 13:39:06 +01:00
dr. M.S. (Matthijs) Berends	c9de74c81a	(v1.4.0.9059) ab class selector fix	2021-01-04 12:29:25 +01:00
dr. M.S. (Matthijs) Berends	82cfa24ea4	(v1.4.0.9058) GH actions update	2021-01-04 11:27:37 +01:00
dr. M.S. (Matthijs) Berends	daac96fefe	(v1.4.0.9057) GH actions update	2021-01-04 09:49:42 +01:00
dr. M.S. (Matthijs) Berends	63a4dda467	(v1.4.0.9056) subsetting ab class selectors for base R	2021-01-03 23:40:05 +01:00
dr. M.S. (Matthijs) Berends	ecac443f86	(v1.4.0.9055) more unit tests	2020-12-31 13:44:58 +01:00
dr. M.S. (Matthijs) Berends	10dec96058	(v1.4.0.9054) unit test update	2020-12-29 22:06:01 +01:00
dr. M.S. (Matthijs) Berends	d3f007bf65	(v1.4.0.9053) unit test old R versions	2020-12-29 21:23:01 +01:00
dr. M.S. (Matthijs) Berends	526f8afb08	(v1.4.0.9052) replaced all sapply's with type-safe vapply's	2020-12-28 22:24:33 +01:00
dr. M.S. (Matthijs) Berends	ccf13dd6c0	(v1.4.0.9051) ab class	2020-12-27 23:19:41 +01:00
dr. M.S. (Matthijs) Berends	afc325c314	(v1.4.0.9050) ab selectors base R	2020-12-27 20:32:40 +01:00
dr. M.S. (Matthijs) Berends	175e33abba	(v1.4.0.9049) unit tests	2020-12-27 15:07:01 +01:00
dr. M.S. (Matthijs) Berends	ed198916bf	(v1.4.0.9048) AmpC de-repressed cephalo-resistant mutants	2020-12-27 14:23:11 +01:00
dr. M.S. (Matthijs) Berends	8b52f9b1be	(v1.4.0.9047) unit tests	2020-12-27 00:30:28 +01:00
dr. M.S. (Matthijs) Berends	acbd0cf7ca	(v1.4.0.9046) get_episode	2020-12-27 00:07:00 +01:00
dr. M.S. (Matthijs) Berends	291f802be3	(v1.4.0.9045) unit tests	2020-12-25 00:06:11 +01:00
dr. M.S. (Matthijs) Berends	df37584189	(v1.4.0.9044) mo tibble printing, mo_shortname() fix	2020-12-24 23:29:10 +01:00
dr. M.S. (Matthijs) Berends	128ebcfd62	(v1.4.0.9043) documentation update	2020-12-22 00:51:17 +01:00
dr. M.S. (Matthijs) Berends	ee70883246	(v1.4.0.9042) auto dark theme website	2020-12-21 22:46:29 +01:00
dr. M.S. (Matthijs) Berends	81af41da3a	(v1.4.0.9041) updates based on review	2020-12-17 16:22:25 +01:00
dr. M.S. (Matthijs) Berends	1faa816090	(v1.4.0.9040) LA-MRSA / CA-MRSA	2020-12-16 16:18:53 +01:00
dr. M.S. (Matthijs) Berends	203bc20eb0	(v1.4.0.9039) more unit tests	2020-12-13 20:44:32 +01:00
dr. M.S. (Matthijs) Berends	ac22b8d5c1	(v1.4.0.9038) docu update	2020-12-13 13:44:04 +01:00
dr. M.S. (Matthijs) Berends	c8bcecf232	(v1.4.0.9037) random_* functions	2020-12-12 23:17:29 +01:00
dr. M.S. (Matthijs) Berends	2edd3339db	(v1.4.0.9036) more unit tests	2020-12-11 12:17:23 +01:00
dr. M.S. (Matthijs) Berends	23ddc6004b	(v1.4.0.9035) eucast_rules fix	2020-12-11 09:05:03 +01:00
dr. M.S. (Matthijs) Berends	c9fc7e8a45	(v1.4.0.9034) MIC printing update	2020-12-09 09:40:50 +01:00
dr. M.S. (Matthijs) Berends	2145f1d1ba	(v1.4.0.9033) documentation update	2020-12-08 12:37:25 +01:00
dr. M.S. (Matthijs) Berends	1bdb136b3a	(v1.4.0.9032) auto-data guessing for functions	2020-12-07 16:06:42 +01:00
dr. M.S. (Matthijs) Berends	fdf29e6c5b	(v1.4.0.9031) as.ab() fix	2020-12-03 22:30:14 +01:00
dr. M.S. (Matthijs) Berends	e03b3c96d3	(v1.4.0.9030) as.mo() fix for known lab codes	2020-12-03 16:59:04 +01:00
dr. M.S. (Matthijs) Berends	4c114ff4b4	(v1.4.0.9029) grey background for backticks, like readr pkg	2020-12-01 16:59:57 +01:00
dr. M.S. (Matthijs) Berends	00447c6dc4	git update	2020-11-28 22:15:44 +01:00
dr. M.S. (Matthijs) Berends	0e1fdb7dd7	(v1.4.0.9027) docu update	2020-11-25 20:26:02 +01:00
dr. M.S. (Matthijs) Berends	7b42b15c90	(v1.4.0.9026) docu update	2020-11-24 11:47:54 +01:00
dr. M.S. (Matthijs) Berends	b045b571a6	(v1.4.0.9025) is_new_episode()	2020-11-23 21:50:27 +01:00
dr. M.S. (Matthijs) Berends	363218da7e	(v1.4.0.9024) is_new_episode()	2020-11-17 16:57:41 +01:00
dr. M.S. (Matthijs) Berends	0800d33228	(v1.4.0.9023) unit tests	2020-11-17 11:53:56 +01:00
dr. M.S. (Matthijs) Berends	93428415d0	(v1.4.0.9022) small bugfix	2020-11-16 20:02:20 +01:00
dr. M.S. (Matthijs) Berends	deefce9520	(v1.4.0.9021) more robust class setting	2020-11-16 16:57:55 +01:00
dr. M.S. (Matthijs) Berends	05fb213a7c	(v1.4.0.9020) mo_is_intrinsic_resistant	2020-11-16 11:03:24 +01:00
dr. M.S. (Matthijs) Berends	9666b78ea7	(v1.4.0.9019) documentation update	2020-11-12 11:07:23 +01:00
dr. M.S. (Matthijs) Berends	f2edac3b67	(v1.4.0.9018) reference_data in as.rsi()	2020-11-11 18:54:03 +01:00
dr. M.S. (Matthijs) Berends	01d9522434	(v1.4.0.9017) stringsAsFactors definitions	2020-11-11 16:49:27 +01:00
dr. M.S. (Matthijs) Berends	68ac39aa7f	(v1.4.0.9016) as.rsi() older R versions	2020-11-10 19:59:14 +01:00
dr. M.S. (Matthijs) Berends	15c732703d	(v1.4.0.9015) bugfix	2020-11-10 16:35:56 +01:00
dr. M.S. (Matthijs) Berends	dd5a0319ef	(v1.4.0.9014) bugfix	2020-11-09 15:18:36 +01:00
dr. M.S. (Matthijs) Berends	d3b1d33210	(v1.4.0.9013) is_gram_negative/positive update	2020-11-09 13:07:02 +01:00
dr. M.S. (Matthijs) Berends	423879c034	(v1.4.0.9012) reference_df fix	2020-11-05 01:11:49 +01:00
dr. M.S. (Matthijs) Berends	5a607abb36	(v1.4.0.9011) message formatting	2020-10-27 15:56:51 +01:00
dr. M.S. (Matthijs) Berends	98773aa859	(v1.4.0.9010) GH actions update	2020-10-26 15:56:15 +01:00
dr. M.S. (Matthijs) Berends	3d096d96c2	(v1.4.0.9009) bugfix for older R version	2020-10-26 15:53:31 +01:00
dr. M.S. (Matthijs) Berends	760d69a3e0	(v1.4.0.9008) like variations	2020-10-26 12:23:03 +01:00
dr. M.S. (Matthijs) Berends	f720c9ba0b	(v1.4.0.9007) bugfix	2020-10-21 15:28:48 +02:00
dr. M.S. (Matthijs) Berends	8f868388ce	(v1.4.0.9006) bugfix	2020-10-21 14:40:00 +02:00
dr. M.S. (Matthijs) Berends	9109b9dd62	(v1.4.0.9005) bugfix	2020-10-21 13:07:23 +02:00
dr. M.S. (Matthijs) Berends	ade9f8bafd	(v1.4.0.9004) bugfix	2020-10-21 11:50:43 +02:00
dr. M.S. (Matthijs) Berends	2ef7dfc8a3	(v1.4.0.9003) CoNS update	2020-10-20 21:00:57 +02:00
dr. M.S. (Matthijs) Berends	ddae8085e1	(v1.4.0.9002) bugfix	2020-10-19 20:44:45 +02:00
dr. M.S. (Matthijs) Berends	4e9ccb4435	(v1.4.0.9001) is_gram_positive(), is_gram_negative(), parameter hardening	2020-10-19 17:09:19 +02:00
dr. M.S. (Matthijs) Berends	833a1be36d	(v1.4.0.9000) more extensive unit tests	2020-10-15 09:46:41 +02:00