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2026-09-16 19:19:40 +02:00 · 2025-06-01 13:43:09 +00:00 · 2025-06-01 10:25:24 +00:00 · 2025-05-21 15:01:25 +00:00 · 2025-05-20 13:14:06 +00:00 · 2025-05-20 13:07:28 +00:00
730 changed files with 54744 additions and 18695 deletions
@@ -1,9 +0,0 @@
-^CRAN-RELEASE$
-^.*\.Rproj$
-^\.Rproj\.user$
-.travis.yml
-appveyor.yml
-.gitlab-ci.yml
-.zenodo.json
-_noinclude
-^cran-comments\.md$
@@ -1,17 +0,0 @@
-.Rproj.user
-.Rhistory
-.RData
-.Ruserdata
-AMR.Rproj
-tests/testthat/Rplots.pdf
-inst/doc
-/src/*.o
-/src/*.o-*
-/src/*.d
-/src/*.so
-_noinclude
-*.dll
-vignettes/*.R
-.DS_Store
-^cran-comments\.md$
-^CRAN-RELEASE$
@@ -1,38 +0,0 @@
-# from https://stackoverflow.com/questions/51866926
-# and https://github.com/jangorecki/r.gitlab.ci
-# and https://docs.gitlab.com/ce/ci/yaml/README.html
-
-# how the Docker+R images work: https://hub.docker.com/r/rocker/r-ver/
-
-variables:
-  WARNINGS_ARE_ERRORS: 1
-
-R 3:
-    image: rocker/r-ver:3 # test on R v3.*.*
-    script:
-    - apt-get update
-    # install dependencies for package
-    - apt-get install --yes --no-install-recommends libxml2-dev libssl-dev libcurl4-openssl-dev zlib1g-dev
-    - Rscript -e 'install.packages(c("devtools", "rlang"))'
-    - Rscript -e 'devtools::install_deps(dependencies = c("Depends", "Imports", "Suggests"), repos = "https://cran.rstudio.com")'
-    # remove vignettes folder and get VignetteBuilder field out of DESCRIPTION file
-    - rm -rf vignettes
-    - Rscript -e 'd <- read.dcf("DESCRIPTION"); d[, colnames(d) == "VignetteBuilder"] <- NA; write.dcf(d, "DESCRIPTION")'
-    # set environmental variable
-    - Rscript -e 'Sys.setenv(NOT_CRAN = "true")'
-    # build package
-    - R CMD build . --no-build-vignettes --no-manual
-    - PKG_FILE_NAME=$(ls -1t *.tar.gz | head -n 1)
-    - R CMD check "${PKG_FILE_NAME}" --no-build-vignettes --no-manual --as-cran
-    # code coverage
-    - apt-get install --yes git
-    - Rscript -e 'cc <- covr::package_coverage(); covr::codecov(coverage = cc, token = "50ffa0aa-fee0-4f8b-a11d-8c7edc6d32ca"); cat("Code coverage:", covr::percent_coverage(cc))'
-    coverage: '/Code coverage: \d+\.\d+/'
-    artifacts:
-      paths:
-      - '*.Rcheck/*.log'
-      - '*.Rcheck/*.out'
-      - '*.Rcheck/*.fail'
-      - '*.Rcheck/*.Rout'
-      name: 'Rcheck log'
-      expire_in: '1 month'
@@ -0,0 +1 @@
+
@@ -0,0 +1,104 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+</head>
+<body>
+    <a href="https://amr-for-r.org/#main" class="visually-hidden-focusable">Skip to contents</a>
+
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+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9288</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
+<li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
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+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="https://amr-for-r.org/reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/authors.html"><span class="fa fa-users"></span> Authors</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="https://amr-for-r.org/logo.svg" class="logo" alt=""><h1>Page not found (404)</h1>
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+Content not found. Please use links in the navbar.
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+</div>
+
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+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer>
+</div>
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+  </body>
+</html>
@@ -0,0 +1 @@
+amr-for-r.org
@@ -1,70 +0,0 @@
-Package: AMR
-Version: 0.5.0
-Date: 2018-12-01
-Title: Antimicrobial Resistance Analysis
-Authors@R: c(
-    person(
-        given = c("Matthijs", "S."),
-        family = "Berends",
-        email = "m.s.berends@umcg.nl",
-        role = c("aut", "cre"),
-        comment = c(ORCID = "0000-0001-7620-1800")),
-    person(
-        given = c("Christian", "F."),
-        family = "Luz",
-        email = "c.f.luz@umcg.nl",
-        role = c("aut", "rev"),
-        comment = c(ORCID = "0000-0001-5809-5995")),
-    person(
-        given = c("Erwin", "E.A."),
-        family = "Hassing",
-        email = "e.hassing@certe.nl",
-        role = "ctb"),
-    person(
-        given = "Corinna",
-        family = "Glasner",
-        email = "c.glasner@umcg.nl",
-        role = "ths",
-        comment = c(ORCID = "0000-0003-1241-1328")),
-    person(
-        given = c("Alex", "W."),
-        family = "Friedrich",
-        email = "alex.friedrich@umcg.nl",
-        role = "ths",
-        comment = c(ORCID = "0000-0003-4881-038X")),
-    person(
-        given = "Bhanu",
-        family = "Sinha",
-        email = "b.sinha@umcg.nl",
-        role = "ths",
-        comment = c(ORCID = "0000-0003-1634-0010")))
-Description: Functions to simplify the analysis and prediction of Antimicrobial 
-    Resistance (AMR) to work with microbial and antimicrobial properties by using
-    evidence-based methods.
-Depends:
-    R (>= 3.1.0)
-Imports:
-    backports,
-    curl,
-    crayon (>= 1.3.0),
-    data.table (>= 1.9.0),
-    dplyr (>= 0.7.0),
-    hms,
-    knitr (>= 1.0.0),
-    rlang (>= 0.2.0),
-    rvest (>= 0.3.2),
-    tidyr (>= 0.7.0),
-    xml2 (>= 1.0.0)
-Suggests:
-    covr (>= 3.0.1),
-    ggplot2,
-    rmarkdown,
-    rstudioapi,
-    testthat (>= 1.0.2)
-VignetteBuilder: knitr
-URL: https://gitlab.com/msberends/AMR
-BugReports: https://gitlab.com/msberends/AMR/issues
-License: GPL-2 | file LICENSE
-Encoding: UTF-8
-LazyData: true
-RoxygenNote: 6.1.1
@@ -1,339 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 2, June 1991
-
- Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
- 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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-conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License.  If you cannot
-distribute so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you
-may not distribute the Program at all.  For example, if a patent
-license would not permit royalty-free redistribution of the Program by
-all those who receive copies directly or indirectly through you, then
-the only way you could satisfy both it and this License would be to
-refrain entirely from distribution of the Program.
-
-If any portion of this section is held invalid or unenforceable under
-any particular circumstance, the balance of the section is intended to
-apply and the section as a whole is intended to apply in other
-circumstances.
-
-It is not the purpose of this section to induce you to infringe any
-patents or other property right claims or to contest validity of any
-such claims; this section has the sole purpose of protecting the
-integrity of the free software distribution system, which is
-implemented by public license practices.  Many people have made
-generous contributions to the wide range of software distributed
-through that system in reliance on consistent application of that
-system; it is up to the author/donor to decide if he or she is willing
-to distribute software through any other system and a licensee cannot
-impose that choice.
-
-This section is intended to make thoroughly clear what is believed to
-be a consequence of the rest of this License.
-
-  8. If the distribution and/or use of the Program is restricted in
-certain countries either by patents or by copyrighted interfaces, the
-original copyright holder who places the Program under this License
-may add an explicit geographical distribution limitation excluding
-those countries, so that distribution is permitted only in or among
-countries not thus excluded.  In such case, this License incorporates
-the limitation as if written in the body of this License.
-
-  9. The Free Software Foundation may publish revised and/or new versions
-of the General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-Each version is given a distinguishing version number.  If the Program
-specifies a version number of this License which applies to it and "any
-later version", you have the option of following the terms and conditions
-either of that version or of any later version published by the Free
-Software Foundation.  If the Program does not specify a version number of
-this License, you may choose any version ever published by the Free Software
-Foundation.
-
-  10. If you wish to incorporate parts of the Program into other free
-programs whose distribution conditions are different, write to the author
-to ask for permission.  For software which is copyrighted by the Free
-Software Foundation, write to the Free Software Foundation; we sometimes
-make exceptions for this.  Our decision will be guided by the two goals
-of preserving the free status of all derivatives of our free software and
-of promoting the sharing and reuse of software generally.
-
-                            NO WARRANTY
-
-  11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
-FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
-OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
-PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
-OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.  THE ENTIRE RISK AS
-TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.  SHOULD THE
-PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
-REPAIR OR CORRECTION.
-
-  12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
-REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
-INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
-OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
-TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
-YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
-PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGES.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-convey the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    {description}
-    Copyright (C) {year}  {fullname}
-
-    This program is free software; you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation; either version 2 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License along
-    with this program; if not, write to the Free Software Foundation, Inc.,
-    51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
-
-Also add information on how to contact you by electronic and paper mail.
-
-If the program is interactive, make it output a short notice like this
-when it starts in an interactive mode:
-
-    Gnomovision version 69, Copyright (C) year name of author
-    Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, the commands you use may
-be called something other than `show w' and `show c'; they could even be
-mouse-clicks or menu items--whatever suits your program.
-
-You should also get your employer (if you work as a programmer) or your
-school, if any, to sign a "copyright disclaimer" for the program, if
-necessary.  Here is a sample; alter the names:
-
-  Yoyodyne, Inc., hereby disclaims all copyright interest in the program
-  `Gnomovision' (which makes passes at compilers) written by James Hacker.
-
-  {signature of Ty Coon}, 1 April 1989
-  Ty Coon, President of Vice
-
-This General Public License does not permit incorporating your program into
-proprietary programs.  If your program is a subroutine library, you may
-consider it more useful to permit linking proprietary applications with the
-library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.
@@ -0,0 +1,319 @@
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9288</small>
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>License</h1>
+
+    </div>
+
+<pre>GNU GENERAL PUBLIC LICENSE
+Version 2, June 1991
+
+Copyright (C) 1989, 1991 Free Software Foundation, Inc., &lt;http://fsf.org/&gt;
+  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+Everyone is permitted to copy and distribute verbatim copies
+of this license document, but changing it is not allowed.
+
+A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
+
+This R package, with package name 'AMR':
+- May be used for commercial purposes
+- May be used for private purposes
+- May NOT be used for patent purposes
+- May be modified, although:
+  - Modifications MUST be released under the same license when distributing the package
+  - Changes made to the code MUST be documented
+- May be distributed, although:
+  - Source code MUST be made available when the package is distributed
+  - A copy of the license and copyright notice MUST be included with the package.
+- Comes with a LIMITATION of liability
+- Comes with NO warranty
+
+END OF THE SUMMARY
+
+
+GNU GENERAL PUBLIC LICENSE
+TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
+
+0. This License applies to any program or other work which contains
+a notice placed by the copyright holder saying it may be distributed
+under the terms of this General Public License. The "Program", below,
+refers to any such program or work, and a "work based on the Program"
+means either the Program or any derivative work under copyright law:
+that is to say, a work containing the Program or a portion of it,
+either verbatim or with modifications and/or translated into another
+language. (Hereinafter, translation is included without limitation in
+the term "modification".) Each licensee is addressed as "you".
+
+Activities other than copying, distribution and modification are not
+covered by this License; they are outside its scope. The act of
+running the Program is not restricted, and the output from the Program
+is covered only if its contents constitute a work based on the
+Program (independent of having been made by running the Program).
+Whether that is true depends on what the Program does.
+
+1. You may copy and distribute verbatim copies of the Program's
+source code as you receive it, in any medium, provided that you
+conspicuously and appropriately publish on each copy an appropriate
+copyright notice and disclaimer of warranty; keep intact all the
+notices that refer to this License and to the absence of any warranty;
+and give any other recipients of the Program a copy of this License
+along with the Program.
+
+You may charge a fee for the physical act of transferring a copy, and
+you may at your option offer warranty protection in exchange for a fee.
+
+2. You may modify your copy or copies of the Program or any portion
+of it, thus forming a work based on the Program, and copy and
+distribute such modifications or work under the terms of Section 1
+above, provided that you also meet all of these conditions:
+
+    a) You must cause the modified files to carry prominent notices
+    stating that you changed the files and the date of any change.
+
+    b) You must cause any work that you distribute or publish, that in
+    whole or in part contains or is derived from the Program or any
+    part thereof, to be licensed as a whole at no charge to all third
+    parties under the terms of this License.
+
+    c) If the modified program normally reads commands interactively
+    when run, you must cause it, when started running for such
+    interactive use in the most ordinary way, to print or display an
+    announcement including an appropriate copyright notice and a
+    notice that there is no warranty (or else, saying that you provide
+    a warranty) and that users may redistribute the program under
+    these conditions, and telling the user how to view a copy of this
+    License.  (Exception: if the Program itself is interactive but
+    does not normally print such an announcement, your work based on
+    the Program is not required to print an announcement.)
+
+These requirements apply to the modified work as a whole. If
+identifiable sections of that work are not derived from the Program,
+and can be reasonably considered independent and separate works in
+themselves, then this License, and its terms, do not apply to those
+sections when you distribute them as separate works. But when you
+distribute the same sections as part of a whole which is a work based
+on the Program, the distribution of the whole must be on the terms of
+this License, whose permissions for other licensees extend to the
+entire whole, and thus to each and every part regardless of who wrote it.
+
+Thus, it is not the intent of this section to claim rights or contest
+your rights to work written entirely by you; rather, the intent is to
+exercise the right to control the distribution of derivative or
+collective works based on the Program.
+
+In addition, mere aggregation of another work not based on the Program
+with the Program (or with a work based on the Program) on a volume of
+a storage or distribution medium does not bring the other work under
+the scope of this License.
+
+3. You may copy and distribute the Program (or a work based on it,
+under Section 2) in object code or executable form under the terms of
+Sections 1 and 2 above provided that you also do one of the following:
+
+    a) Accompany it with the complete corresponding machine-readable
+    source code, which must be distributed under the terms of Sections
+    1 and 2 above on a medium customarily used for software interchange; or,
+
+    b) Accompany it with a written offer, valid for at least three
+    years, to give any third party, for a charge no more than your
+    cost of physically performing source distribution, a complete
+    machine-readable copy of the corresponding source code, to be
+    distributed under the terms of Sections 1 and 2 above on a medium
+    customarily used for software interchange; or,
+
+    c) Accompany it with the information you received as to the offer
+    to distribute corresponding source code.  (This alternative is
+    allowed only for noncommercial distribution and only if you
+    received the program in object code or executable form with such
+    an offer, in accord with Subsection b above.)
+
+The source code for a work means the preferred form of the work for
+making modifications to it. For an executable work, complete source
+code means all the source code for all modules it contains, plus any
+associated interface definition files, plus the scripts used to
+control compilation and installation of the executable. However, as a
+special exception, the source code distributed need not include
+anything that is normally distributed (in either source or binary
+form) with the major components (compiler, kernel, and so on) of the
+operating system on which the executable runs, unless that component
+itself accompanies the executable.
+
+If distribution of executable or object code is made by offering
+access to copy from a designated place, then offering equivalent
+access to copy the source code from the same place counts as
+distribution of the source code, even though third parties are not
+compelled to copy the source along with the object code.
+
+4. You may not copy, modify, sublicense, or distribute the Program
+except as expressly provided under this License. Any attempt
+otherwise to copy, modify, sublicense or distribute the Program is
+void, and will automatically terminate your rights under this License.
+However, parties who have received copies, or rights, from you under
+this License will not have their licenses terminated so long as such
+parties remain in full compliance.
+
+5. You are not required to accept this License, since you have not
+signed it. However, nothing else grants you permission to modify or
+distribute the Program or its derivative works. These actions are
+prohibited by law if you do not accept this License. Therefore, by
+modifying or distributing the Program (or any work based on the
+Program), you indicate your acceptance of this License to do so, and
+all its terms and conditions for copying, distributing or modifying
+the Program or works based on it.
+
+6. Each time you redistribute the Program (or any work based on the
+Program), the recipient automatically receives a license from the
+original licensor to copy, distribute or modify the Program subject to
+these terms and conditions. You may not impose any further
+restrictions on the recipients' exercise of the rights granted herein.
+You are not responsible for enforcing compliance by third parties to
+this License.
+
+7. If, as a consequence of a court judgment or allegation of patent
+infringement or for any other reason (not limited to patent issues),
+conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License. If you cannot
+distribute so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you
+may not distribute the Program at all. For example, if a patent
+license would not permit royalty-free redistribution of the Program by
+all those who receive copies directly or indirectly through you, then
+the only way you could satisfy both it and this License would be to
+refrain entirely from distribution of the Program.
+
+If any portion of this section is held invalid or unenforceable under
+any particular circumstance, the balance of the section is intended to
+apply and the section as a whole is intended to apply in other
+circumstances.
+
+It is not the purpose of this section to induce you to infringe any
+patents or other property right claims or to contest validity of any
+such claims; this section has the sole purpose of protecting the
+integrity of the free software distribution system, which is
+implemented by public license practices. Many people have made
+generous contributions to the wide range of software distributed
+through that system in reliance on consistent application of that
+system; it is up to the author/donor to decide if he or she is willing
+to distribute software through any other system and a licensee cannot
+impose that choice.
+
+This section is intended to make thoroughly clear what is believed to
+be a consequence of the rest of this License.
+
+8. If the distribution and/or use of the Program is restricted in
+certain countries either by patents or by copyrighted interfaces, the
+original copyright holder who places the Program under this License
+may add an explicit geographical distribution limitation excluding
+those countries, so that distribution is permitted only in or among
+countries not thus excluded. In such case, this License incorporates
+the limitation as if written in the body of this License.
+
+9. The Free Software Foundation may publish revised and/or new versions
+of the General Public License from time to time. Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+Each version is given a distinguishing version number. If the Program
+specifies a version number of this License which applies to it and "any
+later version", you have the option of following the terms and conditions
+either of that version or of any later version published by the Free
+Software Foundation. If the Program does not specify a version number of
+this License, you may choose any version ever published by the Free Software
+Foundation.
+
+10. If you wish to incorporate parts of the Program into other free
+programs whose distribution conditions are different, write to the author
+to ask for permission. For software which is copyrighted by the Free
+Software Foundation, write to the Free Software Foundation; we sometimes
+make exceptions for this. Our decision will be guided by the two goals
+of preserving the free status of all derivatives of our free software and
+of promoting the sharing and reuse of software generally.
+
+NO WARRANTY
+
+11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
+FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
+OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
+PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
+OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
+TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
+PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
+REPAIR OR CORRECTION.
+
+12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
+REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
+INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
+OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
+TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
+YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
+PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGES.
+
+END OF TERMS AND CONDITIONS
+</pre>
+
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
@@ -1,245 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method(as.data.frame,atc)
-S3method(as.data.frame,bactid)
-S3method(as.data.frame,frequency_tbl)
-S3method(as.data.frame,mo)
-S3method(as.double,mic)
-S3method(as.integer,mic)
-S3method(as.numeric,mic)
-S3method(as.vector,frequency_tbl)
-S3method(as_tibble,frequency_tbl)
-S3method(barplot,mic)
-S3method(barplot,rsi)
-S3method(diff,frequency_tbl)
-S3method(format,frequency_tbl)
-S3method(hist,frequency_tbl)
-S3method(kurtosis,data.frame)
-S3method(kurtosis,default)
-S3method(kurtosis,matrix)
-S3method(plot,frequency_tbl)
-S3method(plot,mic)
-S3method(plot,rsi)
-S3method(print,atc)
-S3method(print,bactid)
-S3method(print,frequency_tbl)
-S3method(print,mic)
-S3method(print,mo)
-S3method(print,rsi)
-S3method(pull,atc)
-S3method(pull,bactid)
-S3method(pull,mo)
-S3method(skewness,data.frame)
-S3method(skewness,default)
-S3method(skewness,matrix)
-S3method(summary,mic)
-S3method(summary,rsi)
-export("%like%")
-export(EUCAST_rules)
-export(ab_atc)
-export(ab_certe)
-export(ab_name)
-export(ab_official)
-export(ab_property)
-export(ab_tradenames)
-export(ab_trivial_nl)
-export(ab_umcg)
-export(abname)
-export(anti_join_microorganisms)
-export(as.atc)
-export(as.bactid)
-export(as.mic)
-export(as.mo)
-export(as.rsi)
-export(atc_ddd)
-export(atc_groups)
-export(atc_property)
-export(brmo)
-export(count_I)
-export(count_IR)
-export(count_R)
-export(count_S)
-export(count_SI)
-export(count_all)
-export(count_df)
-export(eucast_exceptional_phenotypes)
-export(eucast_rules)
-export(facet_rsi)
-export(first_isolate)
-export(freq)
-export(frequency_tbl)
-export(full_join_microorganisms)
-export(g.test)
-export(geom_rsi)
-export(get_locale)
-export(ggplot_rsi)
-export(guess_atc)
-export(guess_bactid)
-export(guess_mo)
-export(inner_join_microorganisms)
-export(interpretive_reading)
-export(is.atc)
-export(is.bactid)
-export(is.mic)
-export(is.mo)
-export(is.rsi)
-export(is.rsi.eligible)
-export(key_antibiotics)
-export(key_antibiotics_equal)
-export(kurtosis)
-export(labels_rsi_count)
-export(left_join_microorganisms)
-export(like)
-export(mdro)
-export(mo_TSN)
-export(mo_authors)
-export(mo_class)
-export(mo_family)
-export(mo_fullname)
-export(mo_genus)
-export(mo_gramstain)
-export(mo_kingdom)
-export(mo_order)
-export(mo_phylum)
-export(mo_property)
-export(mo_ref)
-export(mo_shortname)
-export(mo_species)
-export(mo_subkingdom)
-export(mo_subspecies)
-export(mo_taxonomy)
-export(mo_type)
-export(mo_year)
-export(mrgn)
-export(n_rsi)
-export(p.symbol)
-export(portion_I)
-export(portion_IR)
-export(portion_R)
-export(portion_S)
-export(portion_SI)
-export(portion_df)
-export(ratio)
-export(read.4D)
-export(resistance_predict)
-export(right_join_microorganisms)
-export(rsi)
-export(rsi_predict)
-export(scale_rsi_colours)
-export(scale_y_percent)
-export(semi_join_microorganisms)
-export(skewness)
-export(theme_rsi)
-export(top_freq)
-exportMethods(as.data.frame.atc)
-exportMethods(as.data.frame.bactid)
-exportMethods(as.data.frame.frequency_tbl)
-exportMethods(as.data.frame.mo)
-exportMethods(as.double.mic)
-exportMethods(as.integer.mic)
-exportMethods(as.numeric.mic)
-exportMethods(as.vector.frequency_tbl)
-exportMethods(as_tibble.frequency_tbl)
-exportMethods(barplot.mic)
-exportMethods(barplot.rsi)
-exportMethods(diff.frequency_tbl)
-exportMethods(format.frequency_tbl)
-exportMethods(hist.frequency_tbl)
-exportMethods(kurtosis)
-exportMethods(kurtosis.data.frame)
-exportMethods(kurtosis.default)
-exportMethods(kurtosis.matrix)
-exportMethods(plot.frequency_tbl)
-exportMethods(plot.mic)
-exportMethods(plot.rsi)
-exportMethods(print.atc)
-exportMethods(print.bactid)
-exportMethods(print.frequency_tbl)
-exportMethods(print.mic)
-exportMethods(print.mo)
-exportMethods(print.rsi)
-exportMethods(pull.atc)
-exportMethods(pull.bactid)
-exportMethods(pull.mo)
-exportMethods(skewness)
-exportMethods(skewness.data.frame)
-exportMethods(skewness.default)
-exportMethods(skewness.matrix)
-exportMethods(summary.mic)
-exportMethods(summary.rsi)
-importFrom(crayon,bgGreen)
-importFrom(crayon,bgRed)
-importFrom(crayon,bgYellow)
-importFrom(crayon,black)
-importFrom(crayon,blue)
-importFrom(crayon,bold)
-importFrom(crayon,green)
-importFrom(crayon,italic)
-importFrom(crayon,red)
-importFrom(crayon,silver)
-importFrom(crayon,strip_style)
-importFrom(curl,nslookup)
-importFrom(data.table,as.data.table)
-importFrom(data.table,data.table)
-importFrom(data.table,setkey)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,all_vars)
-importFrom(dplyr,any_vars)
-importFrom(dplyr,arrange)
-importFrom(dplyr,arrange_at)
-importFrom(dplyr,as_tibble)
-importFrom(dplyr,between)
-importFrom(dplyr,bind_rows)
-importFrom(dplyr,case_when)
-importFrom(dplyr,desc)
-importFrom(dplyr,everything)
-importFrom(dplyr,filter)
-importFrom(dplyr,filter_all)
-importFrom(dplyr,filter_at)
-importFrom(dplyr,full_join)
-importFrom(dplyr,funs)
-importFrom(dplyr,group_by)
-importFrom(dplyr,group_by_at)
-importFrom(dplyr,group_vars)
-importFrom(dplyr,if_else)
-importFrom(dplyr,lag)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,mutate_all)
-importFrom(dplyr,mutate_at)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,progress_estimated)
-importFrom(dplyr,pull)
-importFrom(dplyr,row_number)
-importFrom(dplyr,select)
-importFrom(dplyr,select_if)
-importFrom(dplyr,slice)
-importFrom(dplyr,summarise)
-importFrom(dplyr,summarise_if)
-importFrom(dplyr,tibble)
-importFrom(dplyr,top_n)
-importFrom(dplyr,ungroup)
-importFrom(dplyr,vars)
-importFrom(grDevices,boxplot.stats)
-importFrom(graphics,axis)
-importFrom(graphics,barplot)
-importFrom(graphics,hist)
-importFrom(graphics,plot)
-importFrom(graphics,text)
-importFrom(hms,is.hms)
-importFrom(knitr,kable)
-importFrom(rvest,html_children)
-importFrom(rvest,html_node)
-importFrom(rvest,html_nodes)
-importFrom(rvest,html_table)
-importFrom(stats,complete.cases)
-importFrom(stats,fivenum)
-importFrom(stats,glm)
-importFrom(stats,lm)
-importFrom(stats,mad)
-importFrom(stats,pchisq)
-importFrom(stats,predict)
-importFrom(stats,sd)
-importFrom(utils,browseVignettes)
-importFrom(utils,installed.packages)
-importFrom(xml2,read_html)
@@ -1,317 +0,0 @@
-# 0.5.0
-**Published on CRAN: 2018-12-01**
-
-#### New
-* Repository moved to GitLab: https://gitlab.com/msberends/AMR
-* 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.
-* 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
-
-#### Changed
-* Functions `MDRO`, `BRMO`, `MRGN` and `EUCAST_exceptional_phenotypes` were renamed to `mdro`, `brmo`, `mrgn` and `eucast_exceptional_phenotypes`
-* `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, http://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
-  * 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
-  * 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*
-* Fewer than 3 characters as input for `as.mo` will return NA
-* Function `as.mo` (and all `mo_*` wrappers) now supports genus abbreviations with "species" attached
-  ```r
-  as.mo("E. species")        # B_ESCHR
-  mo_fullname("E. spp.")     # "Escherichia species"
-  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)
-* 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`
-* 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:
-    ```r
-    septic_patients %>% 
-      group_by(hospital_id) %>% 
-      freq(gender)
-    ```
-  * Support for (un)selecting columns:
-    ```r
-    septic_patients %>% 
-      freq(hospital_id) %>% 
-      select(-count, -cum_count) # only get item, percent, cum_percent
-    ```
-  * Check for `hms::is.hms`
-  * 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
-* 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
-* AI improvements for `as.mo`:
-  * `"CRS"` -> *Stenotrophomonas maltophilia*
-  * `"CRSM"` -> *Stenotrophomonas maltophilia*
-  * `"MSSA"` -> *Staphylococcus aureus*
-  * `"MSSE"` -> *Staphylococcus epidermidis*
-* Fix for `join` functions
-* Speed improvement for `is.rsi.eligible`, now 15-20 times faster
-* In `g.test`, when `sum(x)` is below 1000 or any of the expected values is below 5, Fisher's Exact Test will be suggested
-* `ab_name` will try to fall back on `as.atc` when no results are found
-* Removed the addin to view data sets
-* Percentages will now will rounded more logically (e.g. in `freq` function)
-
-#### Other
-* New dependency on package `crayon`, to support formatted text in the console
-* Dependency `tidyr` is now mandatory (went to `Import` field) since `portion_df` and `count_df` rely on it
-* Updated vignettes to comply with README
-
-
-# 0.4.0
-**Published on CRAN: 2018-10-01**
-
-#### 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.
-* New functions based on the existing function `mo_property`:
-  * Taxonomic names: `mo_phylum`, `mo_class`, `mo_order`, `mo_family`, `mo_genus`, `mo_species`, `mo_subspecies`
-  * Semantic names: `mo_fullname`, `mo_shortname`
-  * Microbial properties: `mo_type`, `mo_gramstain`
-  * Author and year: `mo_ref`
-  
-  They also come with support for German, Dutch, French, Italian, Spanish and Portuguese:
-  ```r
-  mo_gramstain("E. coli")
-  # [1] "Gram negative"
-  mo_gramstain("E. coli", language = "de") # German
-  # [1] "Gramnegativ"
-  mo_gramstain("E. coli", language = "es") # Spanish
-  # [1] "Gram negativo"
-  mo_fullname("S. group A", language = "pt") # Portuguese
-  # [1] "Streptococcus grupo A"
-  ```
-  
-  Furthermore, former taxonomic names will give a note about the current taxonomic name:
-  ```r
-  mo_gramstain("Esc blattae")
-  # Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010)
-  # [1] "Gram negative"
-  ```
-* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible isolates
-  * Extra function `count_df` (which works like `portion_df`) to get all counts of S, I and R of a data set with antibiotic columns, with support for grouped variables
-* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_if(is.rsi.eligible, as.rsi)`
-* Functions `as.mo` and `is.mo` as replacements for `as.bactid` and `is.bactid` (since the `microoganisms` data set not only contains bacteria). These last two functions are deprecated and will be removed in a future release. The `as.mo` function determines microbial IDs using Artificial Intelligence (AI):
-  ```r
-  as.mo("E. coli")
-  # [1] B_ESCHR_COL
-  as.mo("MRSA")
-  # [1] B_STPHY_AUR
-  as.mo("S group A")
-  # [1] B_STRPTC_GRA
-  ```
-  And with great speed too - on a quite regular Linux server from 2007 it takes us less than 0.02 seconds to transform 25,000 items:
-  ```r
-  thousands_of_E_colis <- rep("E. coli", 25000)
-  microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = "s")
-  # Unit: seconds
-  #         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
-* 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`
-  * All old syntaxes will still work with this version, but will throw warnings
-* Function `labels_rsi_count` to print datalabels on a RSI `ggplot2` model
-* Functions `as.atc` and `is.atc` to transform/look up antibiotic ATC codes as defined by the WHO. The existing function `guess_atc` is now an alias of `as.atc`.
-
-* Function `ab_property` and its aliases: `ab_name`, `ab_tradenames`, `ab_certe`, `ab_umcg` and `ab_trivial_nl`
-* Introduction to AMR as a vignette
-* Removed clipboard functions as it violated the CRAN policy
-* Renamed `septic_patients$sex` to `septic_patients$gender`
-
-#### Changed
-* Added three antimicrobial agents to the `antibiotics` data set: Terbinafine (D01BA02), Rifaximin (A07AA11) and Isoconazole (D01AC05)
-* Added 163 trade names to the `antibiotics` data set, it now contains 298 different trade names in total, e.g.:
-  ```r
-  ab_official("Bactroban")
-  # [1] "Mupirocin"
-  ab_name(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
-  # [1] "Mupirocin" "Amoxicillin" "Azithromycin" "Flucloxacillin"
-  ab_atc(c("Bactroban", "Amoxil", "Zithromax", "Floxapen"))
-  # [1] "R01AX06" "J01CA04" "J01FA10" "J01CF05"
-  ```
-* For `first_isolate`, rows will be ignored when there's no species available
-* Function `ratio` is now deprecated and will be removed in a future release, as it is not really the scope of this package
-* 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`
-* 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()
-  # which is the same as:
-  septic_patients %>% count_IR(amox, cipr)
-  
-  septic_patients %>% portion_S(amcl)
-  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`.
-* 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
-* Fix for joins, where predefined suffices would not be honoured
-* Added parameter `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`
-  ```r
-  my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2))
-  freq(my_matrix)
-  ```
-  For lists, subsetting is possible:
-  ```r
-  my_list = list(age = septic_patients$age, gender = septic_patients$gender)
-  my_list %>% freq(age)
-  my_list %>% freq(gender)
-  ```
-
-#### Other
-* More unit tests to ensure better integrity of functions
-
-# 0.3.0
-**Published on CRAN: 2018-08-14**
-
-#### 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.
-  * New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
-* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
-  * Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin,  trimethoprim/sulfamethoxazole
-  * Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
-  * Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
-* Support for `ggplot2`
-  * New functions `geom_rsi`, `facet_rsi`, `scale_y_percent`, `scale_rsi_colours` and `theme_rsi`
-  * New wrapper function `ggplot_rsi` to apply all above functions on a data set:
-    * `septic_patients %>% select(tobr, gent) %>% ggplot_rsi` will show portions of S, I and R immediately in a pretty plot
-    * Support for grouped variables, see `?ggplot_rsi`
-* Determining bacterial ID:
-  * New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
-  * The existing function `guess_bactid` is now an alias of `as.bactid`
-  * New Becker classification for *Staphylococcus* to categorise them into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS)
-  * New Lancefield classification for *Streptococcus* to categorise them into Lancefield groups
-* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
-* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
-* ~~Function `ratio` to transform a vector of values to a preset ratio~~
-  * ~~For example: `ratio(c(10, 500, 10), ratio = "1:2:1")` would return `130, 260, 130`~~
-* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
-* Function `p.symbol` to transform p values to their related symbols: `0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1`
-* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the `clipr` package, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)
-* New for frequency tables (function `freq`):
-  * A vignette to explain its usage
-  * Support for `rsi` (antimicrobial resistance) to use as input
-  * Support for `table` to use as input: `freq(table(x, y))`
-  * Support for existing functions `hist` and `plot` to use a frequency table as input: `hist(freq(df$age))`
-  * Support for `as.vector`, `as.data.frame`, `as_tibble` and `format`
-  * Support for quasiquotation: `freq(mydata, mycolumn)` is the same as `mydata %>% freq(mycolumn)`
-  * Function `top_freq` function to return the top/below *n* items as vector
-  * Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
-  * Possibility to globally set the default for the amount of items to print, with `options(max.print.freq = n)` where *n* is your preset value
-
-#### Changed
-* Improvements for forecasting with `resistance_predict` and added more examples
-* More antibiotics added as parameters 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
-* Improved speed of key antibiotics comparison for determining first isolates
-* 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.
-* 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"`
-* Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions:
-  * `as.rsi("<=0.002; S")` will return `S`
-  * `as.mic("<=0.002; S")` will return `<=0.002`
-* Now possible to coerce MIC values with a space between operator and value, i.e. `as.mic("<= 0.002")` now works
-* Classes `rsi` and `mic` do not add the attribute `package.version` anymore
-* Added `"groups"` option for `atc_property(..., property)`. It will return a vector of the ATC hierarchy as defined by the [WHO](https://www.whocc.no/atc/structure_and_principles/). The new function `atc_groups` is a convenient wrapper around this.
-* Build-in host check for `atc_property` as it requires the host set by `url` to be responsive
-* Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable
-* Fix for warning *hybrid evaluation forced for row_number* ([`924b62`](https://github.com/tidyverse/dplyr/commit/924b62)) from the `dplyr` package v0.7.5 and above
-* Support for empty values and for 1 or 2 columns as input for `guess_bactid` (now called `as.bactid`)
-  * So `yourdata %>% select(genus, species) %>% as.bactid()` now also works
-* Other small fixes
-
-#### Other
-* Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
-  * Linux and macOS: https://travis-ci.org/msberends/AMR
-  * Windows: https://ci.appveyor.com/project/msberends/amr
-* Added thesis advisors to DESCRIPTION file
-
-# 0.2.0
-**Published on CRAN: 2018-05-03**
-
-#### New
-* Full support for Windows, Linux and macOS
-* Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)
-* Function `n_rsi` to count cases where antibiotic test results were available, to be used in conjunction with `dplyr::summarise`, see ?rsi
-* Function `guess_bactid` to **determine the ID** of a microorganism based on genus/species or known abbreviations like MRSA
-* Function `guess_atc` to **determine the ATC** of an antibiotic based on name, trade name, or known abbreviations
-* Function `freq` to create **frequency tables**, with additional info in a header
-* Function `MDRO` to **determine Multi Drug Resistant Organisms (MDRO)** with support for country-specific guidelines.
-  * Suggest your own via [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E)
-  * [Exceptional resistances defined by EUCAST](http://www.eucast.org/expert_rules_and_intrinsic_resistance) are also supported instead of countries alone
-  * Functions `BRMO` and `MRGN` are wrappers for Dutch and German guidelines, respectively
-* New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
-* New print format for `tibble`s and `data.table`s
-
-#### Changed
-* Fixed `rsi` class for vectors that contain only invalid antimicrobial interpretations
-* Renamed dataset `ablist` to `antibiotics`
-* Renamed dataset `bactlist` to `microorganisms`
-* Added common abbreviations and trade names to the `antibiotics` dataset
-* Added more microorganisms to the `microorganisms` dataset
-* Added analysis examples on help page of dataset `septic_patients`
-* 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
-* Functions `as.rsi` and `as.mic` now add the package name and version as attributes
-
-#### Other
-* Expanded `README.md` with more examples
-* Added [ORCID](https://orcid.org) of authors to DESCRIPTION file
-* Added unit testing with the `testthat` package
-* 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)
-
-# 0.1.1
-**Published on CRAN: 2018-03-14**
-
-* `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
-* Edited column names to comply with GLIMS, the laboratory information system
-* Added more valid MIC values
-* Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
-* Added barplots for `rsi` and `mic` classes
-
-# 0.1.0
-**Published on CRAN: 2018-02-22**
-
-* First submission to CRAN.
@@ -1,109 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Property of an antibiotic
-#'
-#' Use these functions to return a specific property of an antibiotic from the \code{\link{antibiotics}} data set, based on their ATC code. Get such a code with \code{\link{as.atc}}.
-#' @param x a (vector of a) valid \code{\link{atc}} code or any text that can be coerced to a valid atc with \code{\link{as.atc}}
-#' @param property one of the column names of one of the \code{\link{antibiotics}} data set, like \code{"atc"} and \code{"official"}
-#' @param language language of the returned text, defaults to English (\code{"en"}) and can be set with \code{\link{getOption}("AMR_locale")}. Either one of \code{"en"} (English) or \code{"nl"} (Dutch).
-#' @rdname ab_property
-#' @return A vector of values. In case of \code{ab_tradenames}, if \code{x} is of length one, a vector will be returned. Otherwise a \code{\link{list}}, with \code{x} as names.
-#' @export
-#' @importFrom dplyr %>% left_join pull
-#' @seealso \code{\link{antibiotics}}
-#' @examples
-#' ab_atc("amcl")         # J01CR02
-#' ab_name("amcl")        # Amoxicillin and beta-lactamase inhibitor
-#' ab_name("amcl", "nl")  # Amoxicilline met enzymremmer
-#' ab_trivial_nl("amcl")  # Amoxicilline/clavulaanzuur
-#' ab_certe("amcl")       # amcl
-#' ab_umcg("amcl")        # AMCL
-ab_property <- function(x, property = 'official') {
-  property <- property[1]
-  if (!property %in% colnames(AMR::antibiotics)) {
-    stop("invalid property: ", property, " - use a column name of the `antibiotics` data set")
-  }
-  if (!is.atc(x)) {
-    x <- as.atc(x) # this will give a warning if x cannot be coerced
-  }
-  suppressWarnings(
-    data.frame(atc = x, stringsAsFactors = FALSE) %>%
-      left_join(AMR::antibiotics, by = "atc") %>%
-      pull(property)
-  )
-}
-
-#' @rdname ab_property
-#' @export
-ab_atc <- function(x) {
-  as.character(as.atc(x))
-}
-
-#' @rdname ab_property
-#' @export
-ab_official <- function(x, language = NULL) {
-
-  if (is.null(language)) {
-    language <- getOption("AMR_locale", default = "en")[1L]
-  } else {
-    language <- tolower(language[1])
-  }
-  if (language %in% c("en", "")) {
-    ab_property(x, "official")
-  } else if (language == "nl") {
-    ab_property(x, "official_nl")
-  } else {
-    stop("Unsupported language: '", language, "' - use one of: 'en', 'nl'", call. = FALSE)
-  }
-}
-
-#' @rdname ab_property
-#' @export
-ab_name <- ab_official
-
-#' @rdname ab_property
-#' @export
-ab_trivial_nl <- function(x) {
-  ab_property(x, "trivial_nl")
-}
-
-#' @rdname ab_property
-#' @export
-ab_certe <- function(x) {
-  ab_property(x, "certe")
-}
-
-#' @rdname ab_property
-#' @export
-ab_umcg <- function(x) {
-  ab_property(x, "umcg")
-}
-
-#' @rdname ab_property
-#' @export
-ab_tradenames <- function(x) {
-  res <- ab_property(x, "trade_name")
-  res <- strsplit(res, "|", fixed = TRUE)
-  if (length(x) == 1) {
-    res <- unlist(res)
-  } else {
-    names(res) <- x
-  }
-  res
-}
@@ -1,157 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Name of an antibiotic
-#'
-#' Convert antibiotic codes to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
-#' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
-#' @param from,to type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"certe"} and \code{"umcg"}. When using \code{to = "atc"}, the ATC code will be searched using \code{\link{as.atc}}.
-#' @param textbetween text to put between multiple returned texts
-#' @param tolower return output as lower case with function \code{\link{tolower}}.
-#' @details \strong{The \code{\link{ab_property}} functions are faster and more concise}, but do not support concatenated strings, like \code{abname("AMCL+GENT"}.
-#' @keywords ab antibiotics
-#' @source \code{\link{antibiotics}}
-#' @export
-#' @importFrom dplyr %>% pull
-#' @examples
-#' abname("AMCL")
-#' # "Amoxicillin and beta-lactamase inhibitor"
-#'
-#' # It is quite flexible at default (having `from = "guess"`)
-#' abname(c("amox", "J01CA04", "Trimox", "dispermox", "Amoxil"))
-#' # "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin"
-#'
-#' # Multiple antibiotics can be combined with "+".
-#' # The second antibiotic will be set to lower case when `tolower` was not set:
-#' abname("AMCL+GENT", textbetween = "/")
-#' # "amoxicillin and enzyme inhibitor/gentamicin"
-#'
-#' abname(c("AMCL", "GENT"))
-#' # "Amoxicillin and beta-lactamase inhibitor" "Gentamicin"
-#'
-#' abname("AMCL", to = "trivial_nl")
-#' # "Amoxicilline/clavulaanzuur"
-#'
-#' abname("AMCL", to = "atc")
-#' # "J01CR02"
-#'
-#' # specific codes for University Medical Center Groningen (UMCG):
-#' abname("J01CR02", from = "atc", to = "umcg")
-#' # "AMCL"
-#'
-#' # specific codes for Certe:
-#' abname("J01CR02", from = "atc", to = "certe")
-#' # "amcl"
-abname <- function(abcode,
-                   from = c("guess", "atc", "certe", "umcg"),
-                   to = 'official',
-                   textbetween = ' + ',
-                   tolower = FALSE) {
-
-  if (length(to) != 1L) {
-    stop('`to` must be of length 1', call. = FALSE)
-  }
-
-  if (to == "atc") {
-    return(as.character(as.atc(abcode)))
-  }
-
-  abx <- AMR::antibiotics
-
-  from <- from[1]
-  colnames(abx) <- colnames(abx) %>% tolower()
-  from <- from %>% tolower()
-  to <- to %>% tolower()
-
-  if (!(from %in% colnames(abx) | from == "guess") |
-      !to %in% colnames(abx)) {
-    stop(paste0('Invalid `from` or `to`. Choose one of ',
-                colnames(abx) %>% paste(collapse = ", "), '.'), call. = FALSE)
-  }
-
-  abcode <- as.character(abcode)
-  abcode.bak <- abcode
-
-  for (i in 1:length(abcode)) {
-    if (abcode[i] %like% "[+]") {
-      # support for multiple ab's with +
-      parts <- trimws(strsplit(abcode[i], split = "+", fixed = TRUE)[[1]])
-      ab1 <- abname(parts[1], from = from, to = to)
-      ab2 <- abname(parts[2], from = from, to = to)
-      if (missing(tolower)) {
-        ab2 <- tolower(ab2)
-      }
-      abcode[i] <- paste0(ab1, textbetween, ab2)
-      next
-    }
-    if (from %in% c("atc", "guess")) {
-      if (abcode[i] %in% abx$atc) {
-        abcode[i] <- abx[which(abx$atc == abcode[i]),] %>% pull(to) %>% .[1]
-        next
-      }
-    }
-    if (from %in% c("certe", "guess")) {
-      if (abcode[i] %in% abx$certe) {
-        abcode[i] <- abx[which(abx$certe == abcode[i]),] %>% pull(to) %>% .[1]
-        next
-      }
-    }
-    if (from %in% c("umcg", "guess")) {
-      if (abcode[i] %in% abx$umcg) {
-        abcode[i] <- abx[which(abx$umcg == abcode[i]),] %>% pull(to) %>% .[1]
-        next
-      }
-    }
-    if (from %in% c("trade_name", "guess")) {
-      if (abcode[i] %in% abx$trade_name) {
-        abcode[i] <- abx[which(abx$trade_name == abcode[i]),] %>% pull(to) %>% .[1]
-        next
-      }
-      if (sum(abx$trade_name %like% abcode[i]) > 0) {
-        abcode[i] <- abx[which(abx$trade_name %like% abcode[i]),] %>% pull(to) %>% .[1]
-        next
-      }
-    }
-
-    if (from != "guess") {
-      # when not found, try any `from`
-      abcode[i] <- abx[which(abx[,from] == abcode[i]),] %>% pull(to) %>% .[1]
-    }
-
-    if (is.na(abcode[i]) | length(abcode[i] == 0)) {
-      # try as.atc
-      try(suppressWarnings(
-        abcode[i] <- as.atc(abcode[i])
-      ), silent = TRUE)
-      if (is.na(abcode[i])) {
-        # still not found
-        abcode[i] <- abcode.bak[i]
-        warning('Code "', abcode.bak[i], '" not found in antibiotics list.', call. = FALSE)
-      } else {
-        # fill in the found ATC code
-        abcode[i] <- abname(abcode[i], from = "atc", to = to)
-      }
-    }
-  }
-
-  if (tolower == TRUE) {
-    abcode <- abcode %>% tolower()
-  }
-
-  abcode
-}
@@ -1,372 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-
-#' Transform to ATC code
-#'
-#' Use this function to determine the ATC code of one or more antibiotics. The data set \code{\link{antibiotics}} will be searched for abbreviations, official names and trade names.
-#' @param x character vector to determine \code{ATC} code
-#' @rdname as.atc
-#' @aliases atc
-#' @keywords atc
-#' @export
-#' @importFrom dplyr %>% filter slice pull
-#' @details Use the \code{\link{ab_property}} functions to get properties based on the returned ATC code, see Examples.
-#'
-#' In the ATC classification system, the active substances are classified in a hierarchy with five different levels.  The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups.  The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance.  The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
-#'   Source: \url{https://www.whocc.no/atc/structure_and_principles/}
-#' @return Character (vector) with class \code{"act"}. Unknown values will return \code{NA}.
-#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATCs.
-#' @examples
-#' # These examples all return "J01FA01", the ATC code of Erythromycin:
-#' as.atc("J01FA01")
-#' as.atc("Erythromycin")
-#' as.atc("eryt")
-#' as.atc("   eryt 123")
-#' as.atc("ERYT")
-#' as.atc("ERY")
-#' as.atc("Erythrocin") # Trade name
-#' as.atc("Eryzole")    # Trade name
-#' as.atc("Pediamycin") # Trade name
-#'
-#' # Use ab_* functions to get a specific property based on an ATC code
-#' Cipro <- as.atc("cipro") # returns `J01MA02`
-#' ab_official(Cipro)       # returns "Ciprofloxacin"
-#' ab_umcg(Cipro)           # returns "CIPR", the code used in the UMCG
-as.atc <- function(x) {
-
-  x.new <- rep(NA_character_, length(x))
-  x <- trimws(x, which = "both")
-  # keep only a-z when it's not an ATC code
-  x[!x %like% "[A-Z][0-9]{2}[A-Z]{2}[0-9]{2}"] <- gsub("[^a-zA-Z]+", "", x[!x %like% "[A-Z][0-9]{2}[A-Z]{2}[0-9]{2}"])
-
-  x.bak <- x
-  x <- unique(x[!is.na(x)])
-  failures <- character(0)
-
-  for (i in 1:length(x)) {
-    fail <- TRUE
-
-    # first try atc
-    found <- AMR::antibiotics[which(AMR::antibiotics$atc == x[i]),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # try ATC in ATC code form, even if it does not exist in the antibiotics data set YET
-    if (length(found) == 0 & x[i] %like% '[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]') {
-      warning("ATC code ", x[i], " is not yet in the `antibiotics` data set.")
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- x[i]
-    }
-
-    # try abbreviation of certe and glims
-    found <- AMR::antibiotics[which(tolower(AMR::antibiotics$certe) == tolower(x[i])),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-    found <- AMR::antibiotics[which(tolower(AMR::antibiotics$umcg) == tolower(x[i])),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # try exact official name
-    found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official) == tolower(x[i])),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # try exact official Dutch
-    found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official_nl) == tolower(x[i])),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # try trade name
-    found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$trade_name, ")") %like% x[i]),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # try abbreviation
-    found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$abbr, ")") %like% x[i]),]$atc
-    if (length(found) > 0) {
-      fail <- FALSE
-      x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-    }
-
-    # nothing helped, try first chars of official name, but only if nchar > 4 (cipro, nitro, fosfo)
-    if (nchar(x[i]) > 4) {
-      found <- AMR::antibiotics[which(AMR::antibiotics$official %like% paste0("^", substr(x[i], 1, 5))),]$atc
-      if (length(found) > 0) {
-        fail <- FALSE
-        x.new[is.na(x.new) & x.bak == x[i]] <- found[1L]
-      }
-    }
-
-    # not found
-    if (fail == TRUE) {
-      failures <- c(failures, x[i])
-    }
-  }
-
-  failures <- failures[!failures %in% c(NA, NULL, NaN)]
-  if (length(failures) > 0) {
-    warning("These values could not be coerced to a valid atc: ",
-            paste('"', unique(failures), '"', sep = "", collapse = ', '),
-            ".",
-            call. = FALSE)
-  }
-  class(x.new) <- "atc"
-  x.new
-}
-
-#' @rdname as.atc
-#' @export
-guess_atc <- as.atc
-
-#' @rdname as.atc
-#' @export
-is.atc <- function(x) {
-  identical(class(x), "atc")
-}
-
-
-#' @exportMethod print.atc
-#' @export
-#' @noRd
-print.atc <- function(x, ...) {
-  cat("Class 'atc'\n")
-  print.default(as.character(x), quote = FALSE)
-}
-
-#' @exportMethod as.data.frame.atc
-#' @export
-#' @noRd
-as.data.frame.atc <- function (x, ...) {
-  # same as as.data.frame.character but with removed stringsAsFactors
-  nm <- paste(deparse(substitute(x), width.cutoff = 500L),
-              collapse = " ")
-  if (!"nm" %in% names(list(...))) {
-    as.data.frame.vector(x, ..., nm = nm)
-  } else {
-    as.data.frame.vector(x, ...)
-  }
-}
-
-#' @exportMethod pull.atc
-#' @export
-#' @importFrom dplyr pull
-#' @noRd
-pull.atc <- function(.data, ...) {
-  pull(as.data.frame(.data), ...)
-}
-
-#' Properties of an ATC code
-#'
-#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \cr \strong{This function requires an internet connection.}
-#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
-#' @param property property of an ATC code. Valid values are \code{"ATC"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"}, \code{"Note"} and \code{groups}. For this last option, all hierarchical groups of an ATC code will be returned, see Examples.
-#' @param administration type of administration when using \code{property = "Adm.R"}, see Details
-#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
-#' @param ... parameters to pass on to \code{atc_property}
-#' @details
-#' Options for parameter \code{administration}:
-#' \itemize{
-#'   \item{\code{"Implant"}}{ = Implant}
-#'   \item{\code{"Inhal"}}{ = Inhalation}
-#'   \item{\code{"Instill"}}{ = Instillation}
-#'   \item{\code{"N"}}{ = nasal}
-#'   \item{\code{"O"}}{ = oral}
-#'   \item{\code{"P"}}{ = parenteral}
-#'   \item{\code{"R"}}{ = rectal}
-#'   \item{\code{"SL"}}{ = sublingual/buccal}
-#'   \item{\code{"TD"}}{ = transdermal}
-#'   \item{\code{"V"}}{ = vaginal}
-#' }
-#'
-#' Abbreviations of return values when using \code{property = "U"} (unit):
-#' \itemize{
-#'   \item{\code{"g"}}{ = gram}
-#'   \item{\code{"mg"}}{ = milligram}
-#'   \item{\code{"mcg"}}{ = microgram}
-#'   \item{\code{"U"}}{ = unit}
-#'   \item{\code{"TU"}}{ = thousand units}
-#'   \item{\code{"MU"}}{ = million units}
-#'   \item{\code{"mmol"}}{ = millimole}
-#'   \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
-#' }
-#' @export
-#' @rdname atc_property
-#' @importFrom dplyr %>% progress_estimated
-#' @importFrom xml2 read_html
-#' @importFrom rvest html_children html_node html_nodes html_table
-#' @importFrom curl nslookup
-#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
-#' @examples
-#' \donttest{
-#' # What's the ATC of amoxicillin?
-#' guess_atc("Amoxicillin")
-#' # [1] "J01CA04"
-#'
-#' # oral DDD (Defined Daily Dose) of amoxicillin
-#' atc_property("J01CA04", "DDD", "O")
-#' # parenteral DDD (Defined Daily Dose) of amoxicillin
-#' atc_property("J01CA04", "DDD", "P")
-#'
-#' atc_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
-#' # [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
-#' # [2] "ANTIBACTERIALS FOR SYSTEMIC USE"
-#' # [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
-#' # [4] "Penicillins with extended spectrum"
-#' }
-atc_property <- function(atc_code,
-                         property,
-                         administration = 'O',
-                         url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
-
-  # check active network interface, from https://stackoverflow.com/a/5078002/4575331
-  has_internet <- function(url) {
-    # extract host from given url
-    # https://www.whocc.no/atc_ddd_index/ -> www.whocc.no
-    url <- url %>%
-      gsub("^(http://|https://)", "", .) %>%
-      strsplit('/', fixed = TRUE) %>%
-      unlist() %>%
-      .[1]
-    !is.null(curl::nslookup(url, error = FALSE))
-  }
-  # check for connection using the ATC of amoxicillin
-  if (!has_internet(url = url)) {
-    message("The URL could not be reached.")
-    return(rep(NA, length(atc_code)))
-  }
-
-  if (length(property) != 1L) {
-    stop('`property` must be of length 1', call. = FALSE)
-  }
-  if (length(administration) != 1L) {
-    stop('`administration` must be of length 1', call. = FALSE)
-  }
-
-  # 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)
-
-  if (!property %in% valid_properties) {
-    stop('Invalid `property`, use one of ', paste(valid_properties.bak, collapse = ", "), '.')
-  }
-
-  if (property == 'ddd') {
-    returnvalue <- rep(NA_real_, length(atc_code))
-  } else if (property == 'groups') {
-    returnvalue <- list()
-  } else {
-    returnvalue <- rep(NA_character_, length(atc_code))
-  }
-
-  progress <- progress_estimated(n = length(atc_code))
-
-  for (i in 1:length(atc_code)) {
-
-    progress$tick()$print()
-
-    atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
-
-    if (property == "groups") {
-      tbl <- xml2::read_html(atc_url) %>%
-        rvest::html_node("#content") %>%
-        rvest::html_children() %>%
-        rvest::html_node("a")
-
-      # get URLS of items
-      hrefs <- tbl %>% rvest::html_attr("href")
-      # get text of items
-      texts <- tbl %>% rvest::html_text()
-      # select only text items where URL like "code="
-      texts <- texts[grepl("?code=", tolower(hrefs), fixed = TRUE)]
-      # last one is antibiotics, skip it
-      texts <- texts[1:length(texts) - 1]
-      returnvalue <- c(list(texts), returnvalue)
-
-    } else {
-      tbl <- xml2::read_html(atc_url) %>%
-        rvest::html_nodes('table') %>%
-        rvest::html_table(header = TRUE) %>%
-        as.data.frame(stringsAsFactors = FALSE)
-
-      # case insensitive column names
-      colnames(tbl) <- tolower(colnames(tbl)) %>% gsub('^atc.*', 'atc', .)
-
-      if (length(tbl) == 0) {
-        warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
-        returnvalue[i] <- NA
-        next
-      }
-
-      if (property %in% c('atc', 'name')) {
-        # ATC and name are only in first row
-        returnvalue[i] <- tbl[1, property]
-      } else {
-        if (!'adm.r' %in% colnames(tbl) | is.na(tbl[1, 'adm.r'])) {
-          returnvalue[i] <- NA
-          next
-        } else {
-          for (j in 1:nrow(tbl)) {
-            if (tbl[j, 'adm.r'] == administration) {
-              returnvalue[i] <- tbl[j, property]
-            }
-          }
-        }
-      }
-    }
-  }
-
-  if (property == "groups" & length(returnvalue) == 1) {
-    returnvalue <- returnvalue[[1]]
-  }
-
-  returnvalue
-}
-
-#' @rdname atc_property
-#' @export
-atc_groups <- function(atc_code, ...) {
-  atc_property(atc_code = atc_code, property = "groups", ...)
-}
-
-#' @rdname atc_property
-#' @export
-atc_ddd <- function(atc_code, ...) {
-  atc_property(atc_code = atc_code, property = "ddd", ...)
-}
-
@@ -1,233 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Count isolates
-#'
-#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
-#'
-#' \code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
-#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.
-#' @inheritParams portion
-#' @details These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
-#'
-#' \code{n_rsi} is an alias of \code{count_all}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}}. Their function is equal to \code{count_S(...) + count_IR(...)}.
-#'
-#' \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
-#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
-#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
-#' @keywords resistance susceptibility rsi antibiotics isolate isolates
-#' @return Integer
-#' @rdname count
-#' @name count
-#' @export
-#' @examples
-#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
-#' ?septic_patients
-#'
-#' # Count resistant isolates
-#' count_R(septic_patients$amox)
-#' count_IR(septic_patients$amox)
-#'
-#' # Or susceptibile isolates
-#' count_S(septic_patients$amox)
-#' count_SI(septic_patients$amox)
-#'
-#' # Count all available isolates
-#' count_all(septic_patients$amox)
-#' n_rsi(septic_patients$amox)
-#'
-#' # Since n_rsi counts available isolates, you can
-#' # calculate back to count e.g. non-susceptible isolates.
-#' # This results in the same:
-#' count_IR(septic_patients$amox)
-#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
-#'
-#' library(dplyr)
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(R  = count_R(cipr),
-#'             I  = count_I(cipr),
-#'             S  = count_S(cipr),
-#'             n1 = count_all(cipr), # the actual total; sum of all three
-#'             n2 = n_rsi(cipr),     # same - analogous to n_distinct
-#'             total = n())          # NOT the amount of tested isolates!
-#'
-#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
-#' # so we can see that combination therapy does a lot more than mono therapy.
-#' # Please mind that `portion_S` calculates percentages right away instead.
-#' count_S(septic_patients$amcl)   # S = 1057 (67.1%)
-#' count_all(septic_patients$amcl) # n = 1576
-#'
-#' count_S(septic_patients$gent)   # S = 1372 (74.0%)
-#' count_all(septic_patients$gent) # n = 1855
-#'
-#' with(septic_patients,
-#'      count_S(amcl, gent))       # S = 1396 (92.0%)
-#' with(septic_patients,           # n = 1517
-#'      n_rsi(amcl, gent))
-#'
-#' # Get portions S/I/R immediately of all rsi columns
-#' septic_patients %>%
-#'   select(amox, cipr) %>%
-#'   count_df(translate = FALSE)
-#'
-#' # It also supports grouping variables
-#' septic_patients %>%
-#'   select(hospital_id, amox, cipr) %>%
-#'   group_by(hospital_id) %>%
-#'   count_df(translate = FALSE)
-#'
-count_R <- function(..., also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "R",
-           include_I = FALSE,
-           minimum = 0,
-           as_percent = FALSE,
-           also_single_tested = FALSE,
-           only_count = TRUE)
-}
-
-#' @rdname count
-#' @export
-count_IR <- function(..., also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "R",
-           include_I = TRUE,
-           minimum = 0,
-           as_percent = FALSE,
-           also_single_tested = FALSE,
-           only_count = TRUE)
-}
-
-#' @rdname count
-#' @export
-count_I <- function(..., also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "I",
-           include_I = FALSE,
-           minimum = 0,
-           as_percent = FALSE,
-           also_single_tested = FALSE,
-           only_count = TRUE)
-}
-
-#' @rdname count
-#' @export
-count_SI <- function(..., also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "S",
-           include_I = TRUE,
-           minimum = 0,
-           as_percent = FALSE,
-           also_single_tested = FALSE,
-           only_count = TRUE)
-}
-
-#' @rdname count
-#' @export
-count_S <- function(..., also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "S",
-           include_I = FALSE,
-           minimum = 0,
-           as_percent = FALSE,
-           also_single_tested = FALSE,
-           only_count = TRUE)
-}
-
-#' @rdname count
-#' @export
-count_all <- function(...) {
-  # only print warnings once, if needed
-  count_S(...) + suppressWarnings(count_IR(...))
-}
-
-#' @rdname count
-#' @export
-n_rsi <- function(...) {
-  # only print warnings once, if needed
-  count_S(...) + suppressWarnings(count_IR(...))
-}
-
-#' @rdname count
-#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
-#' @export
-count_df <- function(data,
-                     translate_ab = getOption("get_antibiotic_names", "official"),
-                     combine_IR = FALSE) {
-
-  if (!"data.frame" %in% class(data)) {
-    stop("`count_df` must be called on a data.frame")
-  }
-
-  if (data %>% select_if(is.rsi) %>% ncol() == 0) {
-    stop("No columns with class 'rsi' found. See ?as.rsi.")
-  }
-
-  if (as.character(translate_ab) == "TRUE") {
-    translate_ab <- "official"
-  }
-  options(get_antibiotic_names = translate_ab)
-
-  resS <- summarise_if(.tbl = data,
-                       .predicate = is.rsi,
-                       .funs = count_S) %>%
-    mutate(Interpretation = "S") %>%
-    select(Interpretation, everything())
-
-  if (combine_IR == FALSE) {
-    resI <- summarise_if(.tbl = data,
-                         .predicate = is.rsi,
-                         .funs = count_I) %>%
-      mutate(Interpretation = "I") %>%
-      select(Interpretation, everything())
-
-    resR <- summarise_if(.tbl = data,
-                         .predicate = is.rsi,
-                         .funs = count_R) %>%
-      mutate(Interpretation = "R") %>%
-      select(Interpretation, everything())
-
-    data.groups <- group_vars(data)
-
-    res <- bind_rows(resS, resI, resR) %>%
-      mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
-      tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
-  } else {
-    resIR <- summarise_if(.tbl = data,
-                          .predicate = is.rsi,
-                          .funs = count_IR) %>%
-      mutate(Interpretation = "IR") %>%
-      select(Interpretation, everything())
-
-    data.groups <- group_vars(data)
-
-    res <- bind_rows(resS, resIR) %>%
-      mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
-      tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
-  }
-
-  if (!translate_ab == FALSE) {
-    if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
-      stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
-    }
-    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
-  }
-
-  res
-}
@@ -1,269 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Data set with 423 antibiotics
-#'
-#' A data set containing all antibiotics with a J0 code and some other antimicrobial agents, with their DDDs. Except for trade names and abbreviations, all properties were downloaded from the WHO, see Source.
-#' @format A \code{\link{data.frame}} with 423 observations and 18 variables:
-#' \describe{
-#'   \item{\code{atc}}{ATC code, like \code{J01CR02}}
-#'   \item{\code{certe}}{Certe code, like \code{amcl}}
-#'   \item{\code{umcg}}{UMCG code, like \code{AMCL}}
-#'   \item{\code{abbr}}{Abbreviation as used by many countries, used internally by \code{\link{as.atc}}}
-#'   \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and beta-lactamase inhibitor"}}
-#'   \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
-#'   \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-#'   \item{\code{trade_name}}{Trade name as used by many countries (a total of 294), used internally by \code{\link{as.atc}}}
-#'   \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
-#'   \item{\code{oral_units}}{Units of \code{ddd_units}}
-#'   \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
-#'   \item{\code{iv_units}}{Units of \code{iv_ddd}}
-#'   \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
-#'   \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
-#'   \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
-#'   \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
-#'   \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-#'   \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
-#' }
-#' @source - World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
-#' @seealso \code{\link{microorganisms}}
-# use this later to further fill AMR::antibiotics
-# drug <- "Ciprofloxacin"
-# url <- xml2::read_html(paste0("https://www.ncbi.nlm.nih.gov/pccompound?term=", drug)) %>%
-#   html_nodes(".rslt") %>%
-#   .[[1]] %>%
-#   html_nodes(".title a") %>%
-#   html_attr("href") %>%
-#   gsub("/compound/", "/rest/pug_view/data/compound/", ., fixed = TRUE) %>%
-#   paste0("/XML/?response_type=display")
-# synonyms <- url %>%
-#   read_xml() %>%
-#   xml_contents() %>% .[[6]] %>%
-#   xml_contents() %>% .[[8]] %>%
-#   xml_contents() %>% .[[3]] %>%
-#   xml_contents() %>% .[[3]] %>%
-#   xml_contents() %>%
-#   paste() %>%
-#   .[. %like% "StringValueList"] %>%
-#   gsub("[</]+StringValueList[>]", "", .)
-
-# last two columns created with:
-# antibiotics %>%
-#   mutate(useful_gramnegative =
-#            if_else(
-#              atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
-#                atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
-#                official %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)',
-#              FALSE,
-#              NA
-#            ),
-#          useful_grampositive =
-#            if_else(
-#              atc_group1 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
-#                atc_group2 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
-#                official %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)',
-#              FALSE,
-#              NA
-#            )
-#   )
-#
-# ADD NEW TRADE NAMES FROM OTHER DATAFRAME
-# antibiotics_add_to_property <- function(ab_df, atc, property, value) {
-#   if (length(atc) > 1L) {
-#     stop("only one atc at a time")
-#   }
-#   if (!property %in% c("abbr", "trade_name")) {
-#     stop("only possible for abbr and trade_name")
-#   }
-#
-#   value <- gsub(ab_df[which(ab_df$atc == atc),] %>% pull("official"), "", value, fixed = TRUE)
-#   value <- gsub("||", "|", value, fixed = TRUE)
-#   value <- gsub("[äáàâ]", "a", value)
-#   value <- gsub("[ëéèê]", "e", value)
-#   value <- gsub("[ïíìî]", "i", value)
-#   value <- gsub("[öóòô]", "o", value)
-#   value <- gsub("[üúùû]", "u", value)
-#   if (!atc %in% ab_df$atc) {
-#     message("SKIPPING - UNKNOWN ATC: ", atc)
-#   }
-#   if (is.na(value)) {
-#     message("SKIPPING - VALUE MISSES: ", atc)
-#   }
-#   if (atc %in% ab_df$atc & !is.na(value)) {
-#     current <- ab_df[which(ab_df$atc == atc),] %>% pull(property)
-#     if (!is.na(current)) {
-#       value <- paste(current, value, sep = "|")
-#     }
-#     value <- strsplit(value, "|", fixed = TRUE) %>% unlist() %>% unique() %>% paste(collapse = "|")
-#     value <- gsub("||", "|", value, fixed = TRUE)
-#     # print(value)
-#     ab_df[which(ab_df$atc == atc), property] <- value
-#     message("Added ", value, " to ", ab_official(atc), " (", atc, ", ", ab_certe(atc), ")")
-#   }
-#   ab_df
-# }
-#
-"antibiotics"
-
-#' Data set with taxonomic data from ITIS
-#'
-#' A data set containing the complete microbial taxonomy of the kingdoms Bacteria, Fungi and Protozoa. MO codes can be looked up using \code{\link{as.mo}}.
-#' @inheritSection as.mo ITIS
-#' @format A \code{\link{data.frame}} with 18,833 observations and 15 variables:
-#' \describe{
-#'   \item{\code{mo}}{ID of microorganism}
-#'   \item{\code{tsn}}{Taxonomic Serial Number (TSN), as defined by ITIS}
-#'   \item{\code{genus}}{Taxonomic genus of the microorganism as found in ITIS, see Source}
-#'   \item{\code{species}}{Taxonomic species of the microorganism as found in ITIS, see Source}
-#'   \item{\code{subspecies}}{Taxonomic subspecies of the microorganism as found in ITIS, see Source}
-#'   \item{\code{fullname}}{Full name, like \code{"Echerichia coli"}}
-#'   \item{\code{family}}{Taxonomic family of the microorganism as found in ITIS, see Source}
-#'   \item{\code{order}}{Taxonomic order of the microorganism as found in ITIS, see Source}
-#'   \item{\code{class}}{Taxonomic class of the microorganism as found in ITIS, see Source}
-#'   \item{\code{phylum}}{Taxonomic phylum of the microorganism as found in ITIS, see Source}
-#'   \item{\code{subkingdom}}{Taxonomic subkingdom of the microorganism as found in ITIS, see Source}
-#'   \item{\code{kingdom}}{Taxonomic kingdom of the microorganism as found in ITIS, see Source}
-#'   \item{\code{gramstain}}{Gram of microorganism, like \code{"Gram negative"}}
-#'   \item{\code{prevalence}}{An integer based on estimated prevalence of the microorganism in humans. Used internally by \code{\link{as.mo}}, otherwise quite meaningless. It has a value of 25 for manually added items and a value of 1000 for all unprevalent microorganisms whose genus was somewhere in the top 250 (with another species).}
-#'   \item{\code{ref}}{Author(s) and year of concerning publication as found in ITIS, see Source}
-#' }
-#' @source [3] Integrated Taxonomic Information System (ITIS) on-line database, \url{https://www.itis.gov}.
-#' @seealso \code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms.umcg}}
-"microorganisms"
-
-#' Data set with old taxonomic data from ITIS
-#'
-#' A data set containing old (previously valid or accepted) taxonomic names according to ITIS. This data set is used internally by \code{\link{as.mo}}.
-#' @inheritSection as.mo ITIS
-#' @format A \code{\link{data.frame}} with 2,383 observations and 4 variables:
-#' \describe{
-#'   \item{\code{tsn}}{Old Taxonomic Serial Number (TSN), as defined by ITIS}
-#'   \item{\code{name}}{Old taxonomic name of the microorganism as found in ITIS, see Source}
-#'   \item{\code{tsn_new}}{New Taxonomic Serial Number (TSN), as defined by ITIS}
-#'   \item{\code{ref}}{Author(s) and year of concerning publication as found in ITIS, see Source}
-#' }
-#' @source [3] Integrated Taxonomic Information System (ITIS) on-line database, \url{https://www.itis.gov}.
-#' @seealso \code{\link{as.mo}} \code{\link{mo_property}} \code{\link{microorganisms}}
-"microorganisms.old"
-
-#' Translation table for UMCG
-#'
-#' A data set containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{MO}s with \code{\link{guess_mo}}.
-#' @format A \code{\link{data.frame}} with 1,095 observations and 2 variables:
-#' \describe{
-#'   \item{\code{umcg}}{Code of microorganism according to UMCG MMB}
-#'   \item{\code{certe}}{Code of microorganism according to Certe MMB}
-#' }
-#' @seealso \code{\link{as.mo}} \code{\link{microorganisms.certe}} \code{\link{microorganisms}}
-"microorganisms.umcg"
-
-#' Translation table for Certe
-#'
-#' A data set containing all bacteria codes of Certe MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$mo} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{MO}s with \code{\link{guess_mo}}.
-#' @format A \code{\link{data.frame}} with 2,665 observations and 2 variables:
-#' \describe{
-#'   \item{\code{certe}}{Code of microorganism according to Certe MMB}
-#'   \item{\code{mo}}{Code of microorganism in \code{\link{microorganisms}}}
-#' }
-#' @seealso \code{\link{as.mo}} \code{\link{microorganisms}}
-"microorganisms.certe"
-
-#' Data set with 2000 blood culture isolates of septic patients
-#'
-#' An anonymised data set containing 2,000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
-#' @format A \code{\link{data.frame}} with 2,000 observations and 49 variables:
-#' \describe{
-#'   \item{\code{date}}{date of receipt at the laboratory}
-#'   \item{\code{hospital_id}}{ID of the hospital, from A to D}
-#'   \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
-#'   \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
-#'   \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
-#'   \item{\code{age}}{age of the patient}
-#'   \item{\code{sex}}{sex of the patient}
-#'   \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
-#'   \item{\code{mo}}{ID of microorganism, see \code{\link{microorganisms}}}
-#'   \item{\code{peni:rifa}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{abname}}}
-#' }
-#' @examples
-#' # ----------- #
-#' # PREPARATION #
-#' # ----------- #
-#'
-#' # Save this example data set to an object, so we can edit it:
-#' my_data <- septic_patients
-#'
-#' # load the dplyr package to make data science A LOT easier
-#' library(dplyr)
-#'
-#' # Add first isolates to our data set:
-#' my_data <- my_data %>%
-#'   mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "mo"))
-#'
-#' # -------- #
-#' # ANALYSIS #
-#' # -------- #
-#'
-#' # 1. Get the amoxicillin resistance percentages (p)
-#' #     and numbers (n) of E. coli, divided by hospital:
-#'
-#' my_data %>%
-#'   filter(mo == guess_mo("E. coli"),
-#'          first_isolates == TRUE) %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(n = n_rsi(amox),
-#'             p = portion_IR(amox))
-#'
-#'
-#' # 2. Get the amoxicillin/clavulanic acid resistance
-#' #    percentages of E. coli, trend over the years:
-#'
-#' my_data %>%
-#'   filter(mo == guess_mo("E. coli"),
-#'          first_isolates == TRUE) %>%
-#'   group_by(year = format(date, "%Y")) %>%
-#'   summarise(n = n_rsi(amcl),
-#'             p = portion_IR(amcl, minimum = 20))
-"septic_patients"
-
-#' Supplementary Data
-#'
-#' These \code{\link{data.table}s} are transformed from the \code{\link{microorganisms}} and \code{\link{microorganisms}} data sets to improve speed of \code{\link{as.mo}}. They are meant for internal use only, and are only mentioned here for reference.
-#' @rdname supplementary_data
-#' @name supplementary_data
-# # Renew data:
-# microorganismsDT <- data.table::as.data.table(AMR::microorganisms)
-# # sort on (1) bacteria, (2) fungi, (3) protozoa and then human pathogenic prevalence and then TSN:
-# data.table::setkey(microorganismsDT, kingdom, prevalence, fullname)
-# microorganisms.prevDT <- microorganismsDT[prevalence == 9999,]
-# microorganisms.unprevDT <- microorganismsDT[prevalence != 9999,]
-# microorganisms.oldDT <- data.table::as.data.table(AMR::microorganisms.old)
-# data.table::setkey(microorganisms.oldDT, tsn, name)
-# devtools::use_data(microorganismsDT, overwrite = TRUE)
-# devtools::use_data(microorganisms.prevDT, overwrite = TRUE)
-# devtools::use_data(microorganisms.unprevDT, overwrite = TRUE)
-# devtools::use_data(microorganisms.oldDT, overwrite = TRUE)
-"microorganismsDT"
-
-#' @rdname supplementary_data
-"microorganisms.prevDT"
-
-#' @rdname supplementary_data
-"microorganisms.unprevDT"
-
-#' @rdname supplementary_data
-"microorganisms.oldDT"
@@ -1,95 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Deprecated functions
-#'
-#' These functions are \link{Deprecated}. They will be removed in a future release. Using the functions will give a warning with the name of the function it has been replaced by.
-#' @export
-#' @keywords internal
-#' @name AMR-deprecated
-#' @rdname AMR-deprecated
-as.bactid <- function(...) {
-  .Deprecated("as.mo", package = "AMR")
-  as.mo(...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
-is.bactid <- function(...) {
-  .Deprecated(new = "is.mo", package = "AMR")
-  is.mo(...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
-guess_bactid <- function(...) {
-  .Deprecated(new = "guess_mo", package = "AMR")
-  guess_mo(...)
-}
-
-#' @exportMethod print.bactid
-#' @export
-#' @noRd
-print.bactid <- function(x, ...) {
-  cat("Class 'bactid'\n")
-  print.default(as.character(x), quote = FALSE)
-}
-
-#' @exportMethod as.data.frame.bactid
-#' @export
-#' @noRd
-as.data.frame.bactid <- function (x, ...) {
-  # same as as.data.frame.character but with removed stringsAsFactors
-  nm <- paste(deparse(substitute(x), width.cutoff = 500L),
-              collapse = " ")
-  if (!"nm" %in% names(list(...))) {
-    as.data.frame.vector(x, ..., nm = nm)
-  } else {
-    as.data.frame.vector(x, ...)
-  }
-}
-
-#' @exportMethod pull.bactid
-#' @export
-#' @importFrom dplyr pull
-#' @noRd
-pull.bactid <- function(.data, ...) {
-  pull(as.data.frame(.data), ...)
-}
-
-#' @rdname AMR-deprecated
-#' @export
-ratio <- function(x, ratio) {
-  .Deprecated(package = "AMR")
-
-  if (!all(is.numeric(x))) {
-    stop('`x` must be a vector of numeric values.')
-  }
-  if (length(ratio) == 1) {
-    if (ratio %like% '^([0-9]+([.][0-9]+)?[-,:])+[0-9]+([.][0-9]+)?$') {
-      # support for "1:2:1", "1-2-1", "1,2,1" and even "1.75:2:1.5"
-      ratio <- ratio %>% strsplit("[-,:]") %>% unlist() %>% as.double()
-    } else {
-      stop('Invalid `ratio`: ', ratio, '.')
-    }
-  }
-  if (length(x) != 1 & length(x) != length(ratio)) {
-    stop('`x` and `ratio` must be of same size.')
-  }
-  sum(x, na.rm = TRUE) * (ratio / sum(ratio, na.rm = TRUE))
-}
@@ -1,452 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Determine first (weighted) isolates
-#'
-#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
-#' @param tbl a \code{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 class \code{Date}
-#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' (case insensitive)
-#' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.
-#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
-#' @param col_specimen column name of the specimen type or group
-#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
-#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
-#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
-#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
-#' @param icu_exclude logical whether ICU isolates should be excluded
-#' @param filter_specimen specimen group or type that should be excluded
-#' @param output_logical return output as \code{logical} (will else be the values \code{0} or \code{1})
-#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
-#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
-#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
-#' @param info print progress
-#' @param col_bactid (deprecated, use \code{col_mo} instead)
-#' @param col_genus (deprecated, use \code{col_mo} instead) column name of the genus of the microorganisms
-#' @param col_species (deprecated, use \code{col_mo} instead) column name of the species of the microorganisms
-#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
-#'     To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
-#' @section Key antibiotics:
-#'     There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
-#'
-#'     \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
-#'     Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
-#'
-#'     \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
-#'     A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
-#' @keywords isolate isolates first
-#' @seealso \code{\link{key_antibiotics}}
-#' @export
-#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
-#' @return A vector to add to table, see Examples.
-#' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
-#' @examples
-#' # septic_patients is a dataset available in the AMR package. It is true, genuine data.
-#' ?septic_patients
-#'
-#' library(dplyr)
-#' my_patients <- septic_patients %>%
-#'   mutate(first_isolate = first_isolate(.,
-#'                                        col_date = "date",
-#'                                        col_patient_id = "patient_id",
-#'                                        col_mo = "mo"))
-#'
-#' # Now let's see if first isolates matter:
-#' A <- my_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(count = n_rsi(gent),            # gentamicin availability
-#'             resistance = portion_IR(gent))  # gentamicin resistance
-#'
-#' B <- my_patients %>%
-#'   filter(first_isolate == TRUE) %>%         # the 1st isolate filter
-#'   group_by(hospital_id) %>%
-#'   summarise(count = n_rsi(gent),            # gentamicin availability
-#'             resistance = portion_IR(gent))  # gentamicin resistance
-#'
-#' # Have a look at A and B.
-#' # B is more reliable because every isolate is only counted once.
-#' # Gentamicin resitance in hospital D appears to be 5.4% higher than
-#' # when you (erroneously) would have used all isolates!
-#'
-#' ## OTHER EXAMPLES:
-#'
-#' \dontrun{
-#'
-#' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#'
-#' tbl$first_isolate <-
-#'   first_isolate(tbl)
-#'
-#' tbl$first_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_blood_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood')
-#'
-#' tbl$first_blood_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Blood',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_urine_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine')
-#'
-#' tbl$first_urine_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Urine',
-#'                 col_keyantibiotics = 'keyab')
-#'
-#' tbl$first_resp_isolate <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory')
-#'
-#' tbl$first_resp_isolate_weighed <-
-#'   first_isolate(tbl,
-#'                 filter_specimen = 'Respiratory',
-#'                 col_keyantibiotics = 'keyab')
-#' }
-first_isolate <- function(tbl,
-                          col_date = NULL,
-                          col_patient_id = NULL,
-                          col_mo = NULL,
-                          col_testcode = NULL,
-                          col_specimen = NULL,
-                          col_icu = NULL,
-                          col_keyantibiotics = NULL,
-                          episode_days = 365,
-                          testcodes_exclude = NULL,
-                          icu_exclude = FALSE,
-                          filter_specimen = NULL,
-                          output_logical = TRUE,
-                          type = "keyantibiotics",
-                          ignore_I = TRUE,
-                          points_threshold = 2,
-                          info = TRUE,
-                          col_bactid = NULL,
-                          col_genus = NULL,
-                          col_species = NULL) {
-
-  if (!is.data.frame(tbl)) {
-    stop("`tbl` must be a data frame.", call. = FALSE)
-  }
-
-  # try to find columns based on type
-  # -- mo
-  if (!is.null(col_bactid)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  } else if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
-    message("NOTE: Using column `", col_mo, "` as input for `col_mo`.")
-  }
-  # -- date
-  if (is.null(col_date) & "Date" %in% lapply(tbl, class)) {
-    col_date <- colnames(tbl)[lapply(tbl, class) == "Date"][1]
-    message("NOTE: Using column `", col_date, "` as input for `col_date`.")
-  }
-  # -- patient id
-  if (is.null(col_patient_id) & any(colnames(tbl) %like% "^patient")) {
-    col_patient_id <- colnames(tbl)[colnames(tbl) %like% "^patient"][1]
-    message("NOTE: Using column `", col_patient_id, "` as input for `col_patient_id`.")
-  }
-
-  # bactid OR genus+species must be available
-  if (is.null(col_mo) & (is.null(col_genus) | is.null(col_species))) {
-    stop('`col_mo` or both `col_genus` and `col_species` must be available.')
-  }
-
-
-  # check if columns exist
-  check_columns_existance <- function(column, tblname = tbl) {
-    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
-      stop('Please check tbl for existance.')
-    }
-
-    if (!is.null(column)) {
-      if (!(column %in% colnames(tblname))) {
-        stop('Column `', column, '` not found.')
-      }
-    }
-  }
-
-  check_columns_existance(col_date)
-  check_columns_existance(col_patient_id)
-  check_columns_existance(col_mo)
-  check_columns_existance(col_genus)
-  check_columns_existance(col_species)
-  check_columns_existance(col_testcode)
-  check_columns_existance(col_icu)
-  check_columns_existance(col_keyantibiotics)
-
-  if (!is.null(col_mo)) {
-    # join to microorganisms data set
-    tbl <- tbl %>%
-      mutate_at(vars(col_mo), as.mo) %>%
-      left_join_microorganisms(by = col_mo)
-    col_genus <- "genus"
-    col_species <- "species"
-  }
-
-  if (is.null(col_testcode)) {
-    testcodes_exclude <- NULL
-  }
-  # remove testcodes
-  if (!is.null(testcodes_exclude) & info == TRUE) {
-    cat('[Criteria] Excluded test codes:\n', toString(testcodes_exclude), '\n')
-  }
-
-  if (is.null(col_icu)) {
-    icu_exclude <- FALSE
-  } else {
-    tbl <- tbl %>%
-      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
-  }
-
-  if (is.null(col_specimen)) {
-    filter_specimen <- NULL
-  }
-
-  # filter on specimen group and keyantibiotics when they are filled in
-  if (!is.null(filter_specimen)) {
-    check_columns_existance(col_specimen, tbl)
-    if (info == TRUE) {
-      cat('[Criteria] Excluded other than specimen group \'', filter_specimen, '\'\n', sep = '')
-    }
-  }
-  if (!is.null(col_keyantibiotics)) {
-    tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
-  }
-
-  if (is.null(testcodes_exclude)) {
-    testcodes_exclude <- ''
-  }
-
-  # create new dataframe with original row index and right sorting
-  tbl <- tbl %>%
-    mutate(first_isolate_row_index = 1:nrow(tbl),
-           date_lab = tbl %>% pull(col_date),
-           patient_id = tbl %>% pull(col_patient_id),
-           species = tbl %>% pull(col_species),
-           genus = tbl %>% pull(col_genus)) %>%
-    mutate(species = if_else(is.na(species) | species == "(no MO)", "", species),
-           genus = if_else(is.na(genus) | genus == "(no MO)", "", genus))
-
-  if (is.null(filter_specimen)) {
-    # not filtering on specimen
-    if (icu_exclude == FALSE) {
-      if (info == TRUE & !is.null(col_icu)) {
-        cat('[Criteria] Included isolates from ICU.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      row.start <- 1
-      row.end <- nrow(tbl)
-    } else {
-      if (info == TRUE) {
-        cat('[Criteria] Excluded isolates from ICU.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-
-  } else {
-    # filtering on specimen and only analyse these row to save time
-    if (icu_exclude == FALSE) {
-      if (info == TRUE & !is.null(col_icu)) {
-        cat('[Criteria] Included isolates from ICU.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
-      )
-    } else {
-      if (info == TRUE) {
-        cat('[Criteria] Excluded isolates from ICU.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_specimen,
-                     col_patient_id,
-                     col_genus,
-                     col_species,
-                     col_date))
-      suppressWarnings(
-        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
-                           & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
-      )
-      suppressWarnings(
-        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
-                         & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
-      )
-    }
-
-  }
-
-  if (abs(row.start) == Inf | abs(row.end) == Inf) {
-    if (info == TRUE) {
-      message('No isolates found.')
-    }
-    # NAs where genus is unavailable
-    tbl <- tbl %>%
-      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
-    if (output_logical == FALSE) {
-      tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
-    }
-    return(tbl %>% pull(real_first_isolate))
-  }
-
-  # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
-  suppressWarnings(
-    scope.size <- tbl %>%
-      filter(
-        row_number() %>% between(row.start,
-                                 row.end),
-        genus != "",
-        species != "") %>%
-      nrow()
-  )
-
-  # Analysis of first isolate ----
-  all_first <- tbl %>%
-    mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
-                                     & genus == lag(genus)
-                                     & species == lag(species),
-                                     FALSE,
-                                     TRUE),
-           days_diff = 0) %>%
-    mutate(days_diff = if_else(other_pat_or_mo == FALSE,
-                               (date_lab - lag(date_lab)) + lag(days_diff),
-                               0))
-
-  weighted.notice <- ''
-  if (!is.null(col_keyantibiotics)) {
-    weighted.notice <- 'weighted '
-    if (info == TRUE) {
-      if (type == 'keyantibiotics') {
-        cat('[Criteria] Inclusion based on key antibiotics, ')
-        if (ignore_I == FALSE) {
-          cat('not ')
-        }
-        cat('ignoring I.\n')
-      }
-      if (type == 'points') {
-        cat(paste0('[Criteria] Inclusion based on key antibiotics, using points threshold of '
-                   , points_threshold, '.\n'))
-      }
-    }
-    type_param <- type
-    # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
-    suppressWarnings(
-      all_first <- all_first %>%
-        mutate(key_ab_lag = lag(key_ab)) %>%
-        mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
-                                                     y = key_ab_lag,
-                                                     type = type_param,
-                                                     ignore_I = ignore_I,
-                                                     points_threshold = points_threshold,
-                                                     info = info)) %>%
-        mutate(
-          real_first_isolate =
-            if_else(
-              between(row_number(), row.start, row.end)
-              & genus != ""
-              & species != ""
-              & (other_pat_or_mo
-                 | days_diff >= episode_days
-                 | key_ab_other),
-              TRUE,
-              FALSE))
-    )
-  } else {
-    # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
-    suppressWarnings(
-      all_first <- all_first %>%
-        mutate(
-          real_first_isolate =
-            if_else(
-              between(row_number(), row.start, row.end)
-              & genus != ""
-              & species != ""
-              & (other_pat_or_mo
-                 | days_diff >= episode_days),
-              TRUE,
-              FALSE))
-    )
-  }
-
-  # first one as TRUE
-  all_first[row.start, 'real_first_isolate'] <- TRUE
-  # no tests that should be included, or ICU
-  if (!is.null(col_testcode)) {
-    all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
-  }
-  if (icu_exclude == TRUE) {
-    all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
-  }
-
-  # NAs where genus is unavailable
-  all_first <- all_first %>%
-    mutate(real_first_isolate = if_else(genus %in% c('', '(no MO)', NA), NA, real_first_isolate))
-
-  all_first <- all_first %>%
-    arrange(first_isolate_row_index) %>%
-    pull(real_first_isolate)
-
-  if (info == TRUE) {
-    message(paste0('Found ',
-               all_first %>% sum(na.rm = TRUE),
-               ' first ', weighted.notice, 'isolates (',
-               (all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
-               ' of isolates in scope [where genus was not empty] and ',
-               (all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
-               ' of total)'))
-  }
-
-  if (output_logical == FALSE) {
-    all_first <- all_first %>% as.integer()
-  }
-
-  all_first
-
-}
@@ -1,834 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Frequency table
-#'
-#' Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. \code{top_freq} can be used to get the top/bottom \emph{n} items of a frequency table, with counts as names.
-#' @param x vector of any class or a \code{\link{data.frame}}, \code{\link{tibble}} (may contain a grouping variable) or \code{\link{table}}
-#' @param ... up to nine different columns of \code{x} when \code{x} is a \code{data.frame} or \code{tibble}, to calculate frequencies from - see Examples
-#' @param sort.count sort on count, i.e. frequencies. This will be \code{TRUE} at default for everything except when using grouping variables.
-#' @param nmax number of row to print. The default, \code{15}, uses \code{\link{getOption}("max.print.freq")}. Use \code{nmax = 0}, \code{nmax = Inf}, \code{nmax = NULL} or \code{nmax = NA} to print all rows.
-#' @param na.rm a logical value indicating whether \code{NA} values should be removed from the frequency table. The header (if set) will always print the amount of \code{NA}s.
-#' @param row.names a logical value indicating whether row indices should be printed as \code{1:nrow(x)}
-#' @param markdown a logical value indicating whether the frequency table should be printed in markdown format. This will print all rows and is default behaviour in non-interactive R sessions (like when knitting RMarkdown files).
-#' @param digits how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on \code{\link{getOption}("digits")})
-#' @param quote a logical value indicating whether or not strings should be printed with surrounding quotes
-#' @param header a logical value indicating whether an informative header should be printed
-#' @param title text to show above frequency table, at default to tries to coerce from the variables passed to \code{x}
-#' @param na a character string to should be used to show empty (\code{NA}) values (only useful when \code{na.rm = FALSE})
-#' @param sep a character string to separate the terms when selecting multiple columns
-#' @param f a frequency table
-#' @param n number of top \emph{n} items to return, use -n for the bottom \emph{n} items. It will include more than \code{n} rows if there are ties.
-#' @details Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. This package also has a vignette available to explain the use of this function further, run \code{browseVignettes("AMR")} to read it.
-#'
-#' For numeric values of any class, these additional values will all be calculated with \code{na.rm = TRUE} and shown into the header:
-#' \itemize{
-#'   \item{Mean, using \code{\link[base]{mean}}}
-#'   \item{Standard Deviation, using \code{\link[stats]{sd}}}
-#'   \item{Coefficient of Variation (CV), the standard deviation divided by the mean}
-#'   \item{Mean Absolute Deviation (MAD), using \code{\link[stats]{mad}}}
-#'   \item{Tukey Five-Number Summaries (minimum, Q1, median, Q3, maximum), using \code{\link[stats]{fivenum}}}
-#'   \item{Interquartile Range (IQR) calculated as \code{Q3 - Q1} using the Tukey Five-Number Summaries, i.e. \strong{not} using the \code{\link[stats]{quantile}} function}
-#'   \item{Coefficient of Quartile Variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using the Tukey Five-Number Summaries}
-#'   \item{Outliers (total count and unique count), using \code{\link[grDevices]{boxplot.stats}}}
-#' }
-#'
-#' For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header:
-#' \itemize{
-#'   \item{Oldest, using \code{\link{min}}}
-#'   \item{Newest, using \code{\link{max}}, with difference between newest and oldest}
-#'   \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
-#' }
-#'
-#'
-#' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
-#' @importFrom stats fivenum sd mad
-#' @importFrom grDevices boxplot.stats
-#' @importFrom dplyr %>% arrange arrange_at desc filter_at funs group_by mutate mutate_at n_distinct pull select summarise tibble ungroup vars all_vars
-#' @importFrom utils browseVignettes
-#' @importFrom hms is.hms
-#' @importFrom crayon red green silver
-#' @keywords summary summarise frequency freq
-#' @rdname freq
-#' @name freq
-#' @return A \code{data.frame} (with an additional class \code{"frequency_tbl"}) with five columns: \code{item}, \code{count}, \code{percent}, \code{cum_count} and \code{cum_percent}.
-#' @export
-#' @examples
-#' library(dplyr)
-#'
-#' # this all gives the same result:
-#' freq(septic_patients$hospital_id)
-#' freq(septic_patients[, "hospital_id"])
-#' septic_patients$hospital_id %>% freq()
-#' septic_patients[, "hospital_id"] %>% freq()
-#' septic_patients %>% freq("hospital_id")
-#' septic_patients %>% freq(hospital_id)  #<- easiest to remember (tidyverse)
-#'
-#'
-#' # you could also use `select` or `pull` to get your variables
-#' septic_patients %>%
-#'   filter(hospital_id == "A") %>%
-#'   select(mo) %>%
-#'   freq()
-#'
-#'
-#' # multiple selected variables will be pasted together
-#' septic_patients %>%
-#'   left_join_microorganisms %>%
-#'   filter(hospital_id == "A") %>%
-#'   freq(genus, species)
-#'
-#'
-#' # group a variable and analyse another
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   freq(gender)
-#'
-#'
-#' # get top 10 bugs of hospital A as a vector
-#' septic_patients %>%
-#'   filter(hospital_id == "A") %>%
-#'   freq(mo) %>%
-#'   top_freq(10)
-#'
-#'
-#' # save frequency table to an object
-#' years <- septic_patients %>%
-#'   mutate(year = format(date, "%Y")) %>%
-#'   freq(year)
-#'
-#'
-#' # show only the top 5
-#' years %>% print(nmax = 5)
-#'
-#'
-#' # save to an object with formatted percentages
-#' years <- format(years)
-#'
-#'
-#' # print a histogram of numeric values
-#' septic_patients %>%
-#'   freq(age) %>%
-#'   hist()
-#'
-#'
-#' # or print all points to a regular plot
-#' septic_patients %>%
-#'   freq(age) %>%
-#'   plot()
-#'
-#'
-#' # transform to a data.frame or tibble
-#' septic_patients %>%
-#'   freq(age) %>%
-#'   as.data.frame()
-#'
-#'
-#' # or transform (back) to a vector
-#' septic_patients %>%
-#'   freq(age) %>%
-#'   as.vector()
-#'
-#' identical(septic_patients %>%
-#'             freq(age) %>%
-#'             as.vector() %>%
-#'             sort(),
-#'           sort(septic_patients$age)) # TRUE
-#'
-#'
-#' # it also supports `table` objects
-#' table(septic_patients$gender,
-#'       septic_patients$age) %>%
-#'   freq(sep = " **sep** ")
-#'
-#'
-#' # only get selected columns
-#' septic_patients %>%
-#'   freq(hospital_id) %>%
-#'   select(item, percent)
-#'
-#' septic_patients %>%
-#'   freq(hospital_id) %>%
-#'   select(-count, -cum_count)
-#'
-#'
-#' # check differences between frequency tables
-#' diff(freq(septic_patients$trim),
-#'      freq(septic_patients$trsu))
-frequency_tbl <- function(x,
-                          ...,
-                          sort.count = TRUE,
-                          nmax = getOption("max.print.freq"),
-                          na.rm = TRUE,
-                          row.names = TRUE,
-                          markdown = !interactive(),
-                          digits = 2,
-                          quote = FALSE,
-                          header = !markdown,
-                          title = NULL,
-                          na = "<NA>",
-                          sep = " ") {
-
-  mult.columns <- 0
-  x.group = character(0)
-  df <- NULL
-
-  x.name <- NULL
-  cols <- NULL
-  if (any(class(x) == 'list')) {
-    cols <- names(x)
-    x <- as.data.frame(x, stringsAsFactors = FALSE)
-    x.name <- "a list"
-  } else if (any(class(x) == 'matrix')) {
-    x <- as.data.frame(x, stringsAsFactors = FALSE)
-    x.name <- "a matrix"
-    cols <- colnames(x)
-    if (all(cols %like% 'V[0-9]')) {
-      cols <- NULL
-    }
-  }
-
-  if (any(class(x) == 'data.frame')) {
-    x.group <- group_vars(x)
-    if (length(x.group) > 1) {
-      x.group <- x.group[1L]
-      warning("freq supports one grouping variable, only `", x.group, "` will be kept.", call. = FALSE)
-    }
-
-    if (is.null(x.name)) {
-      x.name <- deparse(substitute(x))
-    }
-    if (x.name == ".") {
-      x.name <- NULL
-    }
-    dots <- base::eval(base::substitute(base::alist(...)))
-    ndots <- length(dots)
-
-    if (ndots < 10) {
-      cols <- as.character(dots)
-      if (!all(cols %in% colnames(x))) {
-        stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
-      }
-      if (length(x.group) > 0) {
-        x.group_cols <- c(x.group, cols)
-        df <- x %>%
-          group_by_at(vars(x.group_cols)) %>%
-          summarise(count = n())
-        if (na.rm == TRUE) {
-          df <- df %>% filter_at(vars(cols), all_vars(!is.na(.)))
-        }
-        if (!missing(sort.count)) {
-          if (sort.count == TRUE) {
-            df <- df %>% arrange_at(c(x.group, "count"), desc)
-          }
-        }
-        df <- df %>%
-          mutate(cum_count = cumsum(count))
-
-        df.topleft <- df[1, 1]
-        df <- df %>%
-          ungroup() %>%
-          # do not repeat group labels
-          mutate_at(vars(x.group), funs(ifelse(lag(.) == ., "", .)))
-        df[1, 1] <- df.topleft
-        colnames(df)[1:2] <- c("group", "item")
-      }
-      if (length(cols) > 0) {
-        x <- x[, cols]
-      }
-    } else if (ndots >= 10) {
-      stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
-    } else {
-      cols <- NULL
-    }
-  } else if (any(class(x) == 'table')) {
-    x <- as.data.frame(x, stringsAsFactors = FALSE)
-    # now this DF contains 3 columns: the 2 vars and a Freq column
-    # paste the first 2 cols and repeat them Freq times:
-    x <- rep(x = do.call(paste, c(x[colnames(x)[1:2]], sep = sep)),
-             times = x$Freq)
-    x.name <- "a `table` object"
-    cols <- NULL
-    #mult.columns <- 2
-  } else {
-    x.name <- NULL
-    cols <- NULL
-  }
-
-  if (!is.null(ncol(x))) {
-    if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
-      x <- x %>% pull(1)
-    } else if (ncol(x) < 10) {
-      mult.columns <- ncol(x)
-      x <- do.call(paste, c(x[colnames(x)], sep = sep))
-    } else {
-      stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
-    }
-  }
-
-  if (mult.columns > 1) {
-    NAs <- x[is.na(x) | x == trimws(strrep('NA ', mult.columns))]
-  } else {
-    NAs <- x[is.na(x)]
-  }
-
-  if (na.rm == TRUE) {
-    x_class <- class(x)
-    x <- x[!x %in% NAs]
-    class(x) <- x_class
-  }
-
-  # if (sort.count == FALSE & 'factor' %in% class(x)) {
-  #   warning("Sorting a factor sorts on factor level, not necessarily alphabetically.", call. = FALSE)
-  # }
-  header_txt <- character(0)
-
-  markdown_line <- ''
-  if (markdown == TRUE) {
-    markdown_line <- '\n'
-  }
-  x_align <- 'l'
-
-  if (mult.columns > 0) {
-    header_txt <- header_txt %>% paste0(markdown_line, 'Columns:   ', mult.columns)
-  } else {
-    header_txt <- header_txt %>% paste0(markdown_line, 'Class:     ', class(x) %>% rev() %>% paste(collapse = " > "))
-    if (!mode(x) %in% class(x)) {
-      header_txt <- header_txt %>% paste0(silver(paste0(" (", mode(x), ")")))
-    }
-  }
-
-  if (NROW(x) > 0) {
-    na_txt <- paste0(NAs %>% length() %>% format(), ' = ',
-                     (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE, round = digits) %>%
-                       sub('NaN', '0', ., fixed = TRUE))
-    if (!na_txt %like% "^0 =") {
-      na_txt <- red(na_txt)
-    } else {
-      na_txt <- green(na_txt)
-    }
-    na_txt <- paste0('(of which NA: ', na_txt, ')')
-  } else {
-    na_txt <- ""
-  }
-
-  header_txt <- header_txt %>% paste0(markdown_line, '\nLength:    ', (NAs %>% length() + x %>% length()) %>% format(),
-                                      ' ', na_txt)
-  header_txt <- header_txt %>% paste0(markdown_line, '\nUnique:    ', x %>% n_distinct() %>% format())
-
-  if (NROW(x) > 0 & any(class(x) == "character")) {
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste0(markdown_line, '\nShortest:  ', x %>% base::nchar() %>% base::min(na.rm = TRUE))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nLongest:   ', x %>% base::nchar() %>% base::max(na.rm = TRUE))
-  }
-
-  if (NROW(x) > 0 & any(class(x) == "difftime") & !is.hms(x)) {
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste(markdown_line, '\nUnits:    ', attributes(x)$units)
-    x <- as.double(x)
-    # after this, the numeric header_txt continues
-  }
-
-  if (NROW(x) > 0 & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
-    # right align number
-    Tukey_five <- stats::fivenum(x, na.rm = TRUE)
-    x_align <- 'r'
-    header_txt <- header_txt %>% paste0('\n')
-    header_txt <- header_txt %>% paste(markdown_line, '\nMean:     ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
-    header_txt <- header_txt %>% paste0(markdown_line, '\nStd. dev.: ', x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits),
-                                        ' (CV: ', x %>% cv(na.rm = TRUE) %>% format(digits = digits),
-                                        ', MAD: ', x %>% stats::mad(na.rm = TRUE) %>% format(digits = digits), ')')
-    header_txt <- header_txt %>% paste0(markdown_line, '\nFive-Num:  ', Tukey_five %>% format(digits = digits) %>% trimws() %>% paste(collapse = ' | '),
-                                        ' (IQR: ', (Tukey_five[4] - Tukey_five[2]) %>% format(digits = digits),
-                                        ', CQV: ', x %>% cqv(na.rm = TRUE) %>% format(digits = digits), ')')
-    outlier_length <- length(boxplot.stats(x)$out)
-    header_txt <- header_txt %>% paste0(markdown_line, '\nOutliers:  ', outlier_length)
-    if (outlier_length > 0) {
-      header_txt <- header_txt %>% paste0(' (unique count: ', boxplot.stats(x)$out %>% n_distinct(), ')')
-    }
-  }
-  if (NROW(x) > 0 & any(class(x) == "rsi")) {
-    header_txt <- header_txt %>% paste0('\n')
-    cnt_S <- sum(x == "S", na.rm = TRUE)
-    cnt_IR <- sum(x %in% c("I", "R"), na.rm = TRUE)
-    header_txt <- header_txt %>% paste(markdown_line, '\n%IR:      ',
-                                       (cnt_IR / sum(!is.na(x), na.rm = TRUE)) %>% percent(force_zero = TRUE, round = digits),
-                                       paste0('(ratio S : IR = 1.0 : ', (cnt_IR / cnt_S) %>% format(digits = 1, nsmall = 1), ")"))
-    if (NROW(x) < 30) {
-      header_txt <- header_txt %>% paste(markdown_line, red('\nToo few isolates for reliable resistance interpretation.'))
-    }
-  }
-
-  formatdates <- "%e %B %Y" # = d mmmm yyyy
-  if (is.hms(x)) {
-    x <- x %>% as.POSIXlt()
-    formatdates <- "%H:%M:%S"
-  }
-  if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
-    header_txt <- header_txt %>% paste0('\n')
-    mindate <- x %>% min(na.rm = TRUE)
-    maxdate <- x %>% max(na.rm = TRUE)
-    maxdate_days <- difftime(maxdate, mindate, units = 'auto') %>% as.double()
-    mediandate <- x %>% median(na.rm = TRUE)
-    median_days <- difftime(mediandate, mindate, units = 'auto') %>% as.double()
-
-    if (formatdates == "%H:%M:%S") {
-      # hms
-      header_txt <- header_txt %>% paste0(markdown_line, '\nEarliest:  ', mindate %>% format(formatdates) %>% trimws())
-      header_txt <- header_txt %>% paste0(markdown_line, '\nLatest:    ', maxdate %>% format(formatdates) %>% trimws(),
-                                          ' (+', difftime(maxdate, mindate, units = 'mins') %>% as.double() %>% format(digits = digits), ' min.)')
-    } else {
-      # other date formats
-      header_txt <- header_txt %>% paste0(markdown_line, '\nOldest:    ', mindate %>% format(formatdates) %>% trimws())
-      header_txt <- header_txt %>% paste0(markdown_line, '\nNewest:    ', maxdate %>% format(formatdates) %>% trimws(),
-                                          ' (+', difftime(maxdate, mindate, units = 'auto') %>% as.double() %>% format(digits = digits), ')')
-    }
-    header_txt <- header_txt %>% paste0(markdown_line, '\nMedian:    ', mediandate %>% format(formatdates) %>% trimws(),
-                                        ' (~', percent(median_days / maxdate_days, round = 0), ')')
-  }
-  if (any(class(x) == 'POSIXlt')) {
-    x <- x %>% format(formatdates)
-  }
-
-  nmax.set <- !missing(nmax)
-  if (!nmax.set & is.null(nmax) & is.null(base::getOption("max.print.freq", default = NULL))) {
-    # default for max print setting
-    nmax <- 15
-  } else if (is.null(nmax)) {
-    nmax <- length(x)
-  }
-
-  if (nmax %in% c(0, Inf, NA, NULL)) {
-    nmax <- length(x)
-  }
-
-  column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent')
-  column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent')
-  column_align <- c(x_align, 'r', 'r', 'r', 'r')
-
-  if (is.null(df)) {
-    # create table with counts and percentages
-    df <- tibble(item = x) %>%
-      group_by(item) %>%
-      summarise(count = n())
-
-    # sort according to setting
-    if (sort.count == TRUE) {
-      df <- df %>% arrange(desc(count), item)
-    } else {
-      df <- df %>% arrange(item)
-    }
-  } else {
-    column_names <- c("Group", column_names)
-    column_names_df <-c("group", column_names_df)
-    column_align <- c("l", column_align)
-  }
-
-  if (df$item %>% paste(collapse = ',') %like% '\033') {
-    # remove escape char
-    # see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
-    df <- df %>% mutate(item = item %>% gsub('\033', ' ', ., fixed = TRUE))
-  }
-
-  if (quote == TRUE) {
-    df$item <- paste0('"', df$item, '"')
-    if (length(x.group) != 0) {
-      df$group <- paste0('"', df$group, '"')
-    }
-  }
-
-  df <- as.data.frame(df, stringsAsFactors = FALSE)
-
-  df$percent <- df$count / base::sum(df$count, na.rm = TRUE)
-  if (length(x.group) == 0) {
-    df$cum_count <- base::cumsum(df$count)
-  }
-  df$cum_percent <- df$cum_count / base::sum(df$count, na.rm = TRUE)
-  if (length(x.group) != 0) {
-    # sort columns
-    df <- df[, column_names_df]
-  }
-
-  if (markdown == TRUE) {
-    tbl_format <- 'markdown'
-  } else {
-    tbl_format <- 'pandoc'
-  }
-
-  if (!is.null(title)) {
-    title <- trimws(gsub("^Frequency table of", "", title[1L], ignore.case = TRUE))
-  }
-
-  structure(.Data = df,
-            class = c('frequency_tbl', class(df)),
-            opt = list(title = title,
-                       data = x.name,
-                       vars = cols,
-                       group_var = x.group,
-                       header = header,
-                       header_txt = header_txt,
-                       row_names = row.names,
-                       column_names = column_names,
-                       column_align = column_align,
-                       tbl_format = tbl_format,
-                       na = na,
-                       nmax = nmax,
-                       nmax.set = nmax.set))
-}
-
-#' @rdname freq
-#' @export
-freq <- frequency_tbl
-
-#' @rdname freq
-#' @export
-#' @importFrom dplyr top_n pull
-top_freq <- function(f, n) {
-  if (!'frequency_tbl' %in% class(f)) {
-    stop('top_freq can only be applied to frequency tables', call. = FALSE)
-  }
-  if (!is.numeric(n) | length(n) != 1L) {
-    stop('For top_freq, `nmax` must be a number of length 1', call. = FALSE)
-  }
-  top <- f %>% top_n(n, count)
-  vect <- top %>% pull(item)
-  names(vect) <- top %>% pull(count)
-  if (length(vect) > abs(n)) {
-    message("top_freq: selecting ", length(vect), " items instead of ", abs(n), ", because of ties")
-  }
-  vect
-}
-
-#' @noRd
-#' @exportMethod diff.frequency_tbl
-#' @importFrom dplyr %>% full_join mutate
-#' @export
-diff.frequency_tbl <- function(x, y, ...) {
-  # check classes
-  if (!"frequency_tbl" %in% class(x)
-      | !"frequency_tbl" %in% class(y)) {
-    stop("Both x and y must be a frequency table.")
-  }
-
-  cat("Differences between frequency tables")
-  if (identical(x, y)) {
-    cat("\n\nNo differences found.\n")
-    return(invisible())
-  }
-
-  x.attr <- attributes(x)$opt
-
-  # only keep item and count
-  x <- x[, 1:2]
-  y <- y[, 1:2]
-
-  x <- x %>%
-    full_join(y,
-              by = colnames(x)[1],
-              suffix = c(".x", ".y")) %>%
-    mutate(
-      diff = case_when(
-        is.na(count.y) ~ -count.x,
-        is.na(count.x) ~ count.y,
-        TRUE ~ count.y - count.x)) %>%
-    mutate(
-      diff.percent = percent(
-        diff / count.x,
-        force_zero = TRUE)) %>%
-    mutate(diff = ifelse(diff %like% '^-',
-                         diff,
-                         paste0("+", diff)),
-           diff.percent = ifelse(diff.percent %like% '^-',
-                                 diff.percent,
-                                 paste0("+", diff.percent)))
-
-  print(
-    knitr::kable(x,
-                 format = x.attr$tbl_format,
-                 col.names = c("Item", "Count #1", "Count #2", "Difference", "Diff. percent"),
-                 align = paste0(x.attr$column_align[1], "rrrr"),
-                 padding = 1)
-  )
-}
-
-#' @rdname freq
-#' @exportMethod print.frequency_tbl
-#' @importFrom knitr kable
-#' @importFrom dplyr n_distinct
-#' @importFrom crayon bold silver
-#' @export
-print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default = 15),
-                                markdown = !interactive(), header = !markdown, ...) {
-
-  opt <- attr(x, 'opt')
-
-  if (length(opt$vars) == 0) {
-    opt$vars <- NULL
-  }
-
-  if (is.null(opt$title)) {
-    if (!is.null(opt$data) & !is.null(opt$vars)) {
-      title <- paste0("`", paste0(opt$vars, collapse = "` and `"), "` from ", opt$data)
-    } else if (!is.null(opt$data) & is.null(opt$vars)) {
-      title <- opt$data
-    } else if (is.null(opt$data) & !is.null(opt$vars)) {
-      title <- paste0("`", paste0(opt$vars, collapse = "` and `"), "`")
-    } else {
-      title <- ""
-    }
-    if (title != "" & length(opt$group_var) != 0) {
-      group_var <- paste0("(grouped by `", opt$group_var, "`)")
-      if (opt$tbl_format == "pandoc") {
-        group_var <- silver(group_var)
-      }
-      title <- paste(title, group_var)
-    }
-    title <- trimws(title)
-    if (title == "") {
-      title <- "Frequency table"
-    } else {
-      title <- paste("Frequency table of", trimws(title))
-    }
-  } else {
-    title <- opt$title
-  }
-
-  if (!missing(nmax)) {
-    opt$nmax <- nmax
-    opt$nmax.set <- TRUE
-  }
-  dots <- list(...)
-  if ("markdown" %in% names(dots)) {
-    if (dots$markdown == TRUE) {
-      opt$tbl_format <- "markdown"
-    } else {
-      opt$tbl_format <- "pandoc"
-    }
-  }
-  if (!missing(markdown)) {
-    opt$tbl_format <- "markdown"
-  }
-  if (!missing(header)) {
-    opt$header <- header
-  }
-
-  # bold title
-  if (opt$tbl_format == "pandoc") {
-    title <- bold(title)
-  } else if (opt$tbl_format == "markdown") {
-    title <- paste0("\n**", title, "**")
-  }
-
-  if (opt$header == TRUE) {
-    cat(title, "\n")
-    if (!is.null(opt$header_txt)) {
-      cat(opt$header_txt)
-    }
-  } else if (opt$tbl_format == "markdown") {
-    # do print title as caption in markdown
-    cat("\n", title, sep = "")
-  }
-
-  if (NROW(x) == 0) {
-    cat('\n\nNo observations.\n')
-    return(invisible())
-  }
-
-  # save old NA setting for kable
-  opt.old <- options()$knitr.kable.NA
-  if (is.null(opt$na)) {
-    opt$na <- "<NA>"
-  }
-  options(knitr.kable.NA = opt$na)
-
-  if (nrow(x) > opt$nmax & opt$tbl_format != "markdown") {
-
-    x.rows <- nrow(x)
-    x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), 'count'], na.rm = TRUE)
-    x.printed <- base::sum(x$count) - x.unprinted
-
-    if (opt$nmax.set == TRUE) {
-      nmax <- opt$nmax
-    } else {
-      nmax <- getOption("max.print.freq", default = 15)
-    }
-
-    x <- x[1:nmax,]
-
-    if (opt$nmax.set == TRUE) {
-      footer <- paste('[ reached `nmax = ', opt$nmax, '`', sep = '')
-    } else {
-      footer <- '[ reached getOption("max.print.freq")'
-    }
-    footer <- paste(footer,
-                    ' -- omitted ',
-                    format(x.rows - opt$nmax),
-                    ' entries, n = ',
-                    format(x.unprinted),
-                    ' (',
-                    (x.unprinted / (x.unprinted + x.printed)) %>% percent(force_zero = TRUE),
-                    ') ]\n', sep = '')
-    if (opt$tbl_format == "pandoc") {
-      footer <- silver(footer) # only silver in regular printing
-    }
-  } else {
-    footer <- NULL
-  }
-
-  if ("item" %in% colnames(x)) {
-    if (any(class(x$item) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
-      x$item <- format(x$item)
-    }
-  } else {
-    opt$column_names <- opt$column_names[!opt$column_names == "Item"]
-  }
-  if ("count" %in% colnames(x)) {
-    if (all(x$count == 1)) {
-      warning('All observations are unique.', call. = FALSE)
-    }
-    x$count <- format(x$count)
-  } else {
-    opt$column_names <- opt$column_names[!opt$column_names == "Count"]
-  }
-  if ("percent" %in% colnames(x)) {
-    x$percent <- percent(x$percent, force_zero = TRUE)
-  } else {
-    opt$column_names <- opt$column_names[!opt$column_names == "Percent"]
-  }
-  if ("cum_count" %in% colnames(x)) {
-    x$cum_count <- format(x$cum_count)
-  } else {
-    opt$column_names <- opt$column_names[!opt$column_names == "Cum. Count"]
-  }
-  if ("cum_percent" %in% colnames(x)) {
-    x$cum_percent <- percent(x$cum_percent, force_zero = TRUE)
-  } else {
-    opt$column_names <- opt$column_names[!opt$column_names == "Cum. Percent"]
-  }
-
-  if (opt$tbl_format == "markdown") {
-    cat("\n")
-  }
-
-  print(
-    knitr::kable(x,
-                 format = opt$tbl_format,
-                 row.names = opt$row_names,
-                 col.names = opt$column_names,
-                 align = opt$column_align,
-                 padding = 1)
-  )
-
-  if (!is.null(footer)) {
-    cat(footer)
-  }
-
-  if (opt$tbl_format == "markdown") {
-    cat("\n\n")
-  } else {
-    cat('\n')
-  }
-
-  # reset old kable setting
-  options(knitr.kable.NA = opt.old)
-  return(invisible())
-
-}
-
-#' @noRd
-#' @exportMethod as.data.frame.frequency_tbl
-#' @export
-as.data.frame.frequency_tbl <- function(x, ...) {
-  attr(x, 'package') <- NULL
-  attr(x, 'opt') <- NULL
-  as.data.frame.data.frame(x, ...)
-}
-
-#' @noRd
-#' @exportMethod as_tibble.frequency_tbl
-#' @export
-#' @importFrom dplyr as_tibble
-as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
-  attr(x, 'package') <- NULL
-  attr(x, 'opt') <- NULL
-  as_tibble(x = as.data.frame(x), validate = validate, ..., rownames = rownames)
-}
-
-#' @noRd
-#' @exportMethod hist.frequency_tbl
-#' @export
-#' @importFrom graphics hist
-hist.frequency_tbl <- function(x, breaks = "Sturges", main = NULL, ...) {
-  opt <- attr(x, 'opt')
-  if (!class(x$item) %in% c("numeric", "double", "integer", "Date")) {
-    stop("'x' must be numeric or Date.", call. = FALSE)
-  }
-  if (!is.null(opt$vars)) {
-    title <- opt$vars
-  } else if (!is.null(opt$data)) {
-    title <- opt$data
-  } else {
-    title <- "frequency table"
-  }
-  if (class(x$item) == "Date") {
-    x <- as.Date(as.vector(x), origin = "1970-01-01")
-  } else {
-    x <- as.vector(x)
-  }
-  if (is.null(main)) {
-    main <- paste("Histogram of", title)
-  }
-  hist(x, main = main, xlab = title, ...)
-}
-
-#' @noRd
-#' @exportMethod plot.frequency_tbl
-#' @export
-plot.frequency_tbl <- function(x, y, ...) {
-  opt <- attr(x, 'opt')
-  if (!is.null(opt$vars)) {
-    title <- opt$vars
-  } else {
-    title <- ""
-  }
-  plot(x = x$item, y = x$count, ylab = "Count", xlab = title, ...)
-}
-
-#' @noRd
-#' @exportMethod as.vector.frequency_tbl
-#' @export
-as.vector.frequency_tbl <- function(x, mode = "any") {
-  as.vector(rep(x$item, x$count), mode = mode)
-}
-
-#' @noRd
-#' @exportMethod format.frequency_tbl
-#' @export
-format.frequency_tbl <- function(x, digits = 1, ...) {
-  opt <- attr(x, 'opt')
-  if (opt$nmax.set == TRUE) {
-    nmax <- opt$nmax
-  } else {
-    nmax <- getOption("max.print.freq", default = 15)
-  }
-
-  x <- x[1:nmax,]
-  x$percent <- percent(x$percent, round = digits, force_zero = TRUE)
-  x$cum_percent <- percent(x$cum_percent, round = digits, force_zero = TRUE)
-  base::format.data.frame(x, ...)
-}
@@ -1,233 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' \emph{G}-test for Count Data
-#'
-#' \code{g.test} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link{chisq.test}} but is more reliable [1]. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
-#' @inherit stats::chisq.test params return
-#' @details If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given.
-#'
-#'   If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers.  Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these.  Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
-#'
-#'   The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
-#'
-#'   In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test.
-#'
-#'   In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow.
-#' @section \emph{G}-test of goodness-of-fit (likelihood ratio test):
-#' Use the \emph{G}-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).
-#'
-#' If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link{fisher.test}}).
-#'
-#' The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link{chisq.test}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
-#'
-#' @section \emph{G}-test of independence:
-#' Use the \emph{G}-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.
-#'
-#' It is also possible to do a \emph{G}-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.
-#'
-#' Fisher's exact test (\code{\link{fisher.test}}) is more accurate than the \emph{G}-test of independence when the expected numbers are small, so it is recommend to only use the \emph{G}-test if your total sample size is greater than 1000.
-#'
-#' The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link{chisq.test}}), and they will give approximately the same results.
-#' @section How the test works:
-#' Unlike the exact test of goodness-of-fit (\code{\link{fisher.test}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
-#'
-#' The \emph{G}-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a \emph{G}-statistic is:
-#'
-#' \code{G <- 2 * sum(x * log(x / E))}
-#'
-#' where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated with:
-#'
-#' \code{p <- stats::pchisq(G, df, lower.tail = FALSE)}
-#'
-#' where \code{df} are the degrees of freedom.
-#'
-#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course.
-#' @keywords chi
-#' @seealso \code{\link{chisq.test}}
-#' @references [1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
-#' @source This code is almost identical to \code{\link{chisq.test}}, except that:
-#' \itemize{
-#'   \item{The calculation of the statistic was changed to \code{2 * sum(x * log(x / E))}}
-#'   \item{Yates' continuity correction was removed as it does not apply to a \emph{G}-test}
-#'   \item{The possibility to simulate p values with \code{simulate.p.value} was removed}
-#' }
-#' @export
-#' @importFrom stats pchisq complete.cases
-#' @examples
-#' # = EXAMPLE 1 =
-#' # Shivrain et al. (2006) crossed clearfield rice (which are resistant
-#' # to the herbicide imazethapyr) with red rice (which are susceptible to
-#' # imazethapyr). They then crossed the hybrid offspring and examined the
-#' # F2 generation, where they found 772 resistant plants, 1611 moderately
-#' # resistant plants, and 737 susceptible plants. If resistance is controlled
-#' # by a single gene with two co-dominant alleles, you would expect a 1:2:1
-#' # ratio.
-#'
-#' x <- c(772, 1611, 737)#'
-#' G <- g.test(x, p = c(1, 2, 1) / 4)
-#' # G$p.value = 0.12574.
-#'
-#' # There is no significant difference from a 1:2:1 ratio.
-#' # Meaning: resistance controlled by a single gene with two co-dominant
-#' # alleles, is plausible.
-#'
-#'
-#' # = EXAMPLE 2 =
-#' # Red crossbills (Loxia curvirostra) have the tip of the upper bill either
-#' # right or left of the lower bill, which helps them extract seeds from pine
-#' # cones. Some have hypothesized that frequency-dependent selection would
-#' # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992)
-#' # observed 1752 right-billed and 1895 left-billed crossbills.
-#'
-#' x <- c(1752, 1895)
-#' g.test(x)
-#' # p = 0.01787343
-#'
-#' # There is a significant difference from a 1:1 ratio.
-#' # Meaning: there are significantly more left-billed birds.
-#'
-g.test <- function(x,
-                    y = NULL,
-                    # correct = TRUE,
-                    p = rep(1/length(x), length(x)),
-                    rescale.p = FALSE) {
-  DNAME <- deparse(substitute(x))
-  if (is.data.frame(x))
-    x <- as.matrix(x)
-  if (is.matrix(x)) {
-    if (min(dim(x)) == 1L)
-      x <- as.vector(x)
-  }
-  if (!is.matrix(x) && !is.null(y)) {
-    if (length(x) != length(y))
-      stop("'x' and 'y' must have the same length")
-    DNAME2 <- deparse(substitute(y))
-    xname <- if (length(DNAME) > 1L || nchar(DNAME, "w") >
-                 30)
-      ""
-    else DNAME
-    yname <- if (length(DNAME2) > 1L || nchar(DNAME2, "w") >
-                 30)
-      ""
-    else DNAME2
-    OK <- complete.cases(x, y)
-    x <- factor(x[OK])
-    y <- factor(y[OK])
-    if ((nlevels(x) < 2L) || (nlevels(y) < 2L))
-      stop("'x' and 'y' must have at least 2 levels")
-    x <- table(x, y)
-    names(dimnames(x)) <- c(xname, yname)
-    DNAME <- paste(paste(DNAME, collapse = "\n"), "and",
-                   paste(DNAME2, collapse = "\n"))
-  }
-  if (any(x < 0) || anyNA(x))
-    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")
-  # if (simulate.p.value) {
-  #   setMETH <- function() METHOD <<- paste(METHOD, "with simulated p-value\n\t (based on",
-  #                                          B, "replicates)")
-  #   almost.1 <- 1 - 64 * .Machine$double.eps
-  # }
-  if (is.matrix(x)) {
-    METHOD <- "G-test of independence"
-    nr <- as.integer(nrow(x))
-    nc <- as.integer(ncol(x))
-    if (is.na(nr) || is.na(nc) || is.na(nr * nc))
-      stop("invalid nrow(x) or ncol(x)", domain = NA)
-    sr <- rowSums(x)
-    sc <- colSums(x)
-    E <- outer(sr, sc, "*")/n
-    v <- function(r, c, n) c * r * (n - r) * (n - c)/n^3
-    V <- outer(sr, sc, v, n)
-    dimnames(E) <- dimnames(x)
-    # if (simulate.p.value && all(sr > 0) && all(sc > 0)) {
-    #   setMETH()
-    #   tmp <- .Call(chisq_sim, sr, sc, B, E, PACKAGE = "stats")
-    #   STATISTIC <- 2 * sum(x * log(x / E)) # sum(sort((x - E)^2/E, decreasing = TRUE)) for chisq.test
-    #   PARAMETER <- NA
-    #   PVAL <- (1 + sum(tmp >= almost.1 * STATISTIC))/(B +
-    #                                                     1)
-    # }
-    # else {
-      # if (simulate.p.value)
-      #   warning("cannot compute simulated p-value with zero marginals")
-      # if (correct && nrow(x) == 2L && ncol(x) == 2L) {
-      #   YATES <- min(0.5, abs(x - E))
-      #   if (YATES > 0)
-      #     METHOD <- paste(METHOD, "with Yates' continuity correction")
-      # }
-      # else YATES <- 0
-      STATISTIC <- 2 * sum(x * log(x / E)) # sum((abs(x - E) - YATES)^2/E) for chisq.test
-      PARAMETER <- (nr - 1L) * (nc - 1L)
-      PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
-   # }
-  }
-  else {
-    if (length(dim(x)) > 2L)
-      stop("invalid 'x'")
-    if (length(x) == 1L)
-      stop("'x' must at least have 2 elements")
-    if (length(x) != length(p))
-      stop("'x' and 'p' must have the same number of elements")
-    if (any(p < 0))
-      stop("probabilities must be non-negative.")
-    if (abs(sum(p) - 1) > sqrt(.Machine$double.eps)) {
-      if (rescale.p)
-        p <- p/sum(p)
-      else stop("probabilities must sum to 1.")
-    }
-    METHOD <- "G-test of goodness-of-fit (likelihood ratio test)"
-    E <- n * p
-    V <- n * p * (1 - p)
-    STATISTIC <- 2 * sum(x * log(x / E)) # sum((x - E)^2/E) for chisq.test
-    names(E) <- names(x)
-    # if (simulate.p.value) {
-    #   setMETH()
-    #   nx <- length(x)
-    #   sm <- matrix(sample.int(nx, B * n, TRUE, prob = p),
-    #                nrow = n)
-    #   ss <- apply(sm, 2L, function(x, E, k) {
-    #     sum((table(factor(x, levels = 1L:k)) - E)^2/E)
-    #   }, E = E, k = nx)
-    #   PARAMETER <- NA
-    #   PVAL <- (1 + sum(ss >= almost.1 * STATISTIC))/(B +
-    #                                                    1)
-    # }
-    # else {
-      PARAMETER <- length(x) - 1
-      PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
-    # }
-  }
-  names(STATISTIC) <- "X-squared"
-  names(PARAMETER) <- "df"
-  # if (any(E < 5) && is.finite(PARAMETER))
-  #   warning("G-statistic approximation may be incorrect")
-
-  # suggest fisher.test when total is < 1000 (John McDonald, Handbook of Biological Statistics, 2014)
-  if (sum(x, na.rm = TRUE) < 1000 && is.finite(PARAMETER)) {
-    warning("G-statistic approximation may be incorrect, consider Fisher's Exact test")
-  } else if (any(E < 5) && is.finite(PARAMETER)) {
-    warning("G-statistic approximation may be incorrect, consider Fisher's Exact test")
-  }
-  structure(list(statistic = STATISTIC, parameter = 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")
-}
@@ -1,53 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Get language for AMR
-#'
-#' Determines the system language to be used for language-dependent output of AMR functions, like \code{\link{mo_gramstain}} and \code{\link{mo_type}}.
-#' @details The system language can be overwritten with \code{\link{getOption}("AMR_locale")}.
-#' @section Supported languages:
-#' Supported languages are \code{"en"} (English), \code{"de"} (German), \code{"nl"} (Dutch), \code{"es"} (Spanish), \code{"it"} (Italian), \code{"fr"} (French), and \code{"pt"} (Portuguese).
-#' @export
-get_locale <- function() {
-  if (!is.null(getOption("AMR_locale"))) {
-    if (getOption("AMR_locale") %in% c("en", "de", "nl", "es", "it", "fr", "pt")) {
-      return(getOption("AMR_locale"))
-    }
-  }
-  lang <- Sys.getlocale("LC_COLLATE")
-  # grepl with case = FALSE is faster than like
-  if (grepl("^(English|en_|EN_)", lang, ignore.case = FALSE)) {
-    # as first option to optimise speed
-    "en"
-  } else if (grepl("^(German|Deutsch|de_|DE_)", lang, ignore.case = FALSE)) {
-    "de"
-  } else if (grepl("^(Dutch|Nederlands|nl_|NL_)", lang, ignore.case = FALSE)) {
-    "nl"
-  } else if (grepl("^(Spanish|Espa.ol|es_|ES_)", lang, ignore.case = FALSE)) {
-    "es"
-  } else if (grepl("^(Italian|Italiano|it_|IT_)", lang, ignore.case = FALSE)) {
-    "it"
-  } else if (grepl("^(French|Fran.ais|fr_|FR_)", lang, ignore.case = FALSE)) {
-    "fr"
-  } else if (grepl("^(Portuguese|Portugu.s|pt_|PT_)", lang, ignore.case = FALSE)) {
-    "pt"
-  } else {
-    # other language -> set to English
-    "en"
-  }
-}
@@ -1,337 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' AMR bar plots with \code{ggplot}
-#'
-#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
-#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
-#' @param position position adjustment of bars, either \code{"fill"} (default when \code{fun} is \code{\link{count_df}}), \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"}
-#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
-#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
-#' @param breaks numeric vector of positions
-#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
-#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
-#' @param fun function to transform \code{data}, either \code{\link{count_df}} (default) or \code{\link{portion_df}}
-#' @param nrow (when using \code{facet}) number of rows
-#' @param datalabels show datalabels using \code{labels_rsi_count}, will at default only be shown when \code{fun = count_df}
-#' @param datalabels.size size of the datalabels
-#' @param datalabels.colour colour of the datalabels
-#' @param ... other parameters passed on to \code{geom_rsi}
-#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
-#'
-#' \strong{The functions}\cr
-#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{count_df}} at default, can also be \code{\link{portion_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
-#'
-#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
-#'
-#' \code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.
-#'
-#' \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R, using \code{\link[ggplot2]{scale_brewer}}.
-#'
-#' \code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.
-#'
-#' \code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}}
-#'
-#' \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
-#' @rdname ggplot_rsi
-#' @importFrom utils installed.packages
-#' @export
-#' @examples
-#' library(dplyr)
-#' library(ggplot2)
-#'
-#' # get antimicrobial results for drugs against a UTI:
-#' ggplot(septic_patients %>% select(amox, nitr, fosf, trim, cipr)) +
-#'   geom_rsi()
-#'
-#' # prettify the plot using some additional functions:
-#' df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
-#' ggplot(df) +
-#'   geom_rsi() +
-#'   scale_y_percent() +
-#'   scale_rsi_colours() +
-#'   labels_rsi_count() +
-#'   theme_rsi()
-#'
-#' # or better yet, simplify this using the wrapper function - a single command:
-#' septic_patients %>%
-#'   select(amox, nitr, fosf, trim, cipr) %>%
-#'   ggplot_rsi()
-#'
-#' # get only portions and no counts:
-#' septic_patients %>%
-#'   select(amox, nitr, fosf, trim, cipr) %>%
-#'   ggplot_rsi(fun = portion_df)
-#'
-#' # add other ggplot2 parameters as you like:
-#' septic_patients %>%
-#'   select(amox, nitr, fosf, trim, cipr) %>%
-#'   ggplot_rsi(width = 0.5,
-#'              colour = "black",
-#'              size = 1,
-#'              linetype = 2,
-#'              alpha = 0.25)
-#' \donttest{
-#'
-#' # for colourblind mode, use divergent colours from the viridis package:
-#' septic_patients %>%
-#'   select(amox, nitr, fosf, trim, cipr) %>%
-#'   ggplot_rsi() + scale_fill_viridis_d()
-#'
-#'
-#' # it also supports groups (don't forget to use the group var on `x` or `facet`):
-#' septic_patients %>%
-#'   select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
-#'   group_by(hospital_id) %>%
-#'   ggplot_rsi(x = hospital_id,
-#'              facet = Antibiotic,
-#'              nrow = 1) +
-#'   labs(title = "AMR of Anti-UTI Drugs Per Hospital",
-#'        x = "Hospital")
-#'
-#' # genuine analysis: check 2 most prevalent microorganisms
-#' septic_patients %>%
-#'   # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
-#'   mutate(mo = as.mo(mo, Becker = TRUE)) %>%
-#'   # filter on top three bacterial ID's
-#'   filter(mo %in% top_freq(freq(.$mo), 3)) %>%
-#'   # determine first isolates
-#'   mutate(first_isolate = first_isolate(.,
-#'                                        col_date = "date",
-#'                                        col_patient_id = "patient_id",
-#'                                        col_mo = "mo")) %>%
-#'   # filter on first isolates
-#'   filter(first_isolate == TRUE) %>%
-#'   # get short MO names (like "E. coli")
-#'   mutate(mo = mo_shortname(mo, Becker = TRUE)) %>%
-#'   # select this short name and some antiseptic drugs
-#'   select(mo, cfur, gent, cipr) %>%
-#'   # group by MO
-#'   group_by(mo) %>%
-#'   # plot the thing, putting MOs on the facet
-#'   ggplot_rsi(x = Antibiotic,
-#'              facet = mo,
-#'              translate_ab = FALSE,
-#'              nrow = 1) +
-#'   labs(title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
-#'        subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
-#'        x = "Microorganisms")
-#' }
-ggplot_rsi <- function(data,
-                       position = NULL,
-                       x = "Antibiotic",
-                       fill = "Interpretation",
-                       # params = list(),
-                       facet = NULL,
-                       breaks = seq(0, 1, 0.1),
-                       translate_ab = "official",
-                       fun = count_df,
-                       nrow = NULL,
-                       datalabels = TRUE,
-                       datalabels.size = 3,
-                       datalabels.colour = "grey15",
-                       ...) {
-
-  if (!"ggplot2" %in% rownames(installed.packages())) {
-    stop('this function requires the ggplot2 package.', call. = FALSE)
-  }
-
-  fun_name <- deparse(substitute(fun))
-  if (!fun_name %in% c("portion_df", "count_df")) {
-    stop("`fun` must be portion_df or count_df")
-  }
-
-  x <- x[1]
-  facet <- facet[1]
-
-  # we work with aes_string later on
-  x_deparse <- deparse(substitute(x))
-  if (x_deparse != "x") {
-    x <- x_deparse
-  }
-  if (x %like% '".*"') {
-    x <- substr(x, 2, nchar(x) - 1)
-  }
-  facet_deparse <- deparse(substitute(facet))
-  if (facet_deparse != "facet") {
-    facet <- facet_deparse
-  }
-  if (facet %like% '".*"') {
-    facet <- substr(facet, 2, nchar(facet) - 1)
-  }
-  if (facet %in% c("NULL", "")) {
-    facet <- NULL
-  }
-
-  p <- ggplot2::ggplot(data = data) +
-    geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun, ...) +
-    theme_rsi()
-
-  if (fill == "Interpretation") {
-    # set RSI colours
-    p <- p + scale_rsi_colours()
-  }
-  if (is.null(position)) {
-    position <- "fill"
-  }
-  if (fun_name == "portion_df"
-      | (fun_name == "count_df" & position == "fill")) {
-    # portions, so use y scale with percentage
-    p <- p + scale_y_percent(breaks = breaks)
-  }
-
-  if (fun_name == "count_df" & datalabels == TRUE) {
-    p <- p + labels_rsi_count(position = position,
-                              x = x,
-                              datalabels.size = datalabels.size,
-                              datalabels.colour = datalabels.colour)
-  }
-
-  if (!is.null(facet)) {
-    p <- p + facet_rsi(facet = facet, nrow = nrow)
-  }
-
-  p
-}
-
-#' @rdname ggplot_rsi
-#' @export
-geom_rsi <- function(position = NULL,
-                     x = c("Antibiotic", "Interpretation"),
-                     fill = "Interpretation",
-                     translate_ab = "official",
-                     fun = count_df,
-                     ...)  {
-
-  fun_name <- deparse(substitute(fun))
-  if (!fun_name %in% c("portion_df", "count_df", "fun")) {
-    stop("`fun` must be portion_df or count_df")
-  }
-  y <- "Value"
-  if (identical(fun, count_df)) {
-    if (missing(position) | is.null(position)) {
-      position <- "fill"
-    }
-  } else {
-    if (missing(position) | is.null(position)) {
-      position <- "stack"
-    }
-  }
-
-  x <- x[1]
-
-  # we work with aes_string later on
-  x_deparse <- deparse(substitute(x))
-  if (x_deparse != "x") {
-    x <- x_deparse
-  }
-  if (x %like% '".*"') {
-    x <- substr(x, 2, nchar(x) - 1)
-  }
-
-  if (tolower(x) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
-    x <- "Antibiotic"
-  } else if (tolower(x) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
-    x <- "Interpretation"
-  }
-
-  options(get_antibiotic_names = translate_ab)
-
-  ggplot2::layer(geom = "bar", stat = "identity", position = position,
-                 mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
-                 data = fun, params = list(...))
-
-}
-
-#' @rdname ggplot_rsi
-#' @export
-facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), nrow = NULL) {
-
-   facet <- facet[1]
-
-  # we work with aes_string later on
-  facet_deparse <- deparse(substitute(facet))
-  if (facet_deparse != "facet") {
-    facet <- facet_deparse
-  }
-  if (facet %like% '".*"') {
-    facet <- substr(facet, 2, nchar(facet) - 1)
-  }
-
-  if (tolower(facet) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
-    facet <- "Interpretation"
-  } else if (tolower(facet) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
-    facet <- "Antibiotic"
-  }
-
-  ggplot2::facet_wrap(facets = facet, scales = "free_x", nrow = nrow)
-}
-
-#' @rdname ggplot_rsi
-#' @export
-scale_y_percent <- function(breaks = seq(0, 1, 0.1)) {
-  ggplot2::scale_y_continuous(breaks = breaks,
-                              labels = percent(breaks))
-}
-
-#' @rdname ggplot_rsi
-#' @export
-scale_rsi_colours <- function() {
-  ggplot2::scale_fill_brewer(palette = "RdYlGn")
-}
-
-#' @rdname ggplot_rsi
-#' @export
-theme_rsi <- function() {
-  ggplot2::theme_minimal() +
-    ggplot2::theme(panel.grid.major.x = ggplot2::element_blank(),
-                   panel.grid.minor = ggplot2::element_blank(),
-                   panel.grid.major.y = ggplot2::element_line(colour = "grey75"))
-}
-
-#' @rdname ggplot_rsi
-#' @export
-labels_rsi_count <- function(position = NULL,
-                             x = "Antibiotic",
-                             datalabels.size = 3,
-                             datalabels.colour = "grey15") {
-  if (is.null(position)) {
-    position <- "fill"
-  }
-  if (position == "fill") {
-    position <- ggplot2::position_fill(vjust = 0.5)
-  }
-  ggplot2::geom_text(mapping = ggplot2::aes_string(label = "lbl",
-                                                   x = x,
-                                                   y = "Value"),
-                     position = position,
-                     data = getlbls,
-                     inherit.aes = FALSE,
-                     size = datalabels.size,
-                     colour = datalabels.colour)
-}
-
-#' @importFrom dplyr %>% group_by mutate
-getlbls <- function(data) {
-  data %>%
-    count_df() %>%
-    group_by(Antibiotic) %>%
-    mutate(lbl = paste0(percent(Value / sum(Value, na.rm = TRUE), force_zero = TRUE),
-                        " (n=", Value, ")")) %>%
-    mutate(lbl = ifelse(lbl == "0.0% (n=0)", "", lbl))
-}
@@ -1,81 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-globalVariables(c(".",
-                  "..property",
-                  "antibiotic",
-                  "Antibiotic",
-                  "antibiotics",
-                  "authors",
-                  "Becker",
-                  "cnt",
-                  "count",
-                  "count.x",
-                  "count.y",
-                  "cum_count",
-                  "cum_percent",
-                  "date_lab",
-                  "days_diff",
-                  "diff.percent",
-                  "fctlvl",
-                  "first_isolate_row_index",
-                  "Freq",
-                  "fullname",
-                  "genus",
-                  "gramstain",
-                  "index",
-                  "Interpretation",
-                  "item",
-                  "key_ab",
-                  "key_ab_lag",
-                  "key_ab_other",
-                  "Lancefield",
-                  "lbl",
-                  "median",
-                  "mic",
-                  "microorganisms",
-                  "microorganisms.old",
-                  "microorganismsDT",
-                  "microorganisms.prevDT",
-                  "microorganisms.unprevDT",
-                  "microorganisms.oldDT",
-                  "microorganisms.certe",
-                  "microorganisms.umcg",
-                  "mo",
-                  "mo.old",
-                  "n",
-                  "name",
-                  "observations",
-                  "other_pat_or_mo",
-                  "Pasted",
-                  "patient_id",
-                  "prevalence",
-                  "psae",
-                  "R",
-                  "ref",
-                  "real_first_isolate",
-                  "S",
-                  "septic_patients",
-                  "shortname",
-                  "species",
-                  "tsn",
-                  "tsn_new",
-                  "value",
-                  "Value",
-                  "y",
-                  "year"))
@@ -1,136 +0,0 @@
-#' Join a table with \code{microorganisms}
-#'
-#' Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
-#' @rdname join
-#' @name join
-#' @aliases join inner_join
-#' @param x existing table to join, or character vector
-#' @param by a variable to join by - if left empty will search for a column with class \code{mo} (created with \code{\link{as.mo}}) or will be \code{"mo"} if that column name exists in \code{x}, could otherwise be a column name of \code{x} with values that exist in \code{microorganisms$mo} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
-#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
-#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
-#' @details \strong{Note:} As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
-#' @export
-#' @examples
-#' left_join_microorganisms(as.mo("K. pneumoniae"))
-#' left_join_microorganisms("B_KLBSL_PNE")
-#'
-#' library(dplyr)
-#' septic_patients %>% left_join_microorganisms()
-#'
-#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
-#'                             to = as.Date("2018-01-07"),
-#'                             by = 1),
-#'                  bacteria = as.mo(c("S. aureus", "MRSA", "MSSA", "STAAUR",
-#'                                     "E. coli", "E. coli", "E. coli")),
-#'                  stringsAsFactors = FALSE)
-#' colnames(df)
-#' df_joined <- left_join_microorganisms(df, "bacteria")
-#' colnames(df_joined)
-inner_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  join <- suppressWarnings(
-    dplyr::inner_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
-  )
-  if (nrow(join) > nrow(x)) {
-    warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-left_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  join <- suppressWarnings(
-    dplyr::left_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
-  )
-  if (nrow(join) > nrow(x)) {
-    warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-right_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  join <- suppressWarnings(
-    dplyr::right_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
-  )
-  if (nrow(join) > nrow(x)) {
-    warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-full_join_microorganisms <- function(x, by = NULL, suffix = c("2", ""), ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  join <- suppressWarnings(
-    dplyr::full_join(x = x, y = AMR::microorganisms, by = by, suffix = suffix, ...)
-  )
-  if (nrow(join) > nrow(x)) {
-    warning('The newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original.')
-  }
-  join
-}
-
-#' @rdname join
-#' @export
-semi_join_microorganisms <- function(x, by = NULL, ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  suppressWarnings(
-    dplyr::semi_join(x = x, y = AMR::microorganisms, by = by, ...)
-  )
-}
-
-#' @rdname join
-#' @export
-anti_join_microorganisms <- function(x, by = NULL, ...) {
-  checked <- joins_check_df(x, by)
-  x <- checked$x
-  by <- checked$by
-  suppressWarnings(
-    dplyr::anti_join(x = x, y = AMR::microorganisms, by = by, ...)
-  )
-}
-
-joins_check_df <- function(x, by) {
-  if (!any(class(x) %in% c("data.frame", "matrix"))) {
-    x <- data.frame(mo = as.character(x), stringsAsFactors = FALSE)
-    if (is.null(by)) {
-      by <- "mo"
-    }
-  }
-  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"
-      } else {
-        stop("Cannot join - no column found with name or class  `mo`.", call. = FALSE)
-      }
-    }
-    message('Joining, by = "', by, '"') # message same as dplyr::join functions
-  }
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::microorganisms)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  list(x = x,
-       by = joinby)
-}
@@ -1,263 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Key antibiotics for first \emph{weighted} isolates
-#'
-#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
-#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
-#' @param x,y characters to compare
-#' @inheritParams first_isolate
-#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive
-#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive
-#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive
-#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
-#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it wouldn't.
-#'
-#'   At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
-#'   \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
-#'
-#'   At default, the antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
-#'   \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"gent"}, \code{"tobr"}, \code{"coli"}, \code{"cfot"}, \code{"cfta"}, \code{"mero"}.
-#'
-#'
-#'   The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
-#' @inheritSection first_isolate Key antibiotics
-#' @rdname key_antibiotics
-#' @export
-#' @importFrom dplyr %>% mutate if_else
-#' @seealso \code{\link{first_isolate}}
-#' @examples
-#' # septic_patients is a dataset available in the AMR package
-#' ?septic_patients
-#' my_patients <- septic_patients
-#'
-#' library(dplyr)
-#' # set key antibiotics to a new variable
-#' my_patients <- my_patients %>%
-#'   mutate(keyab = key_antibiotics(.)) %>%
-#'   mutate(
-#'     # now calculate first isolates
-#'     first_regular = first_isolate(., "date", "patient_id", "mo"),
-#'     # and first WEIGHTED isolates
-#'     first_weighted = first_isolate(., "date", "patient_id", "mo",
-#'                                    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)
-#'
-#'
-#' # output of the `key_antibiotics` function could be like this:
-#' strainA <- "SSSRR.S.R..S"
-#' strainB <- "SSSIRSSSRSSS"
-#'
-#' key_antibiotics_equal(strainA, strainB)
-#' # TRUE, because I is ignored (as are missing values)
-#'
-#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
-#' # FALSE, because I is not ignored and so the 4th value differs
-key_antibiotics <- function(tbl,
-                            col_mo = "mo",
-                            universal_1 = "amox",
-                            universal_2 = "amcl",
-                            universal_3 = "cfur",
-                            universal_4 = "pita",
-                            universal_5 = "cipr",
-                            universal_6 = "trsu",
-                            GramPos_1 = "vanc",
-                            GramPos_2 = "teic",
-                            GramPos_3 = "tetr",
-                            GramPos_4 = "eryt",
-                            GramPos_5 = "oxac",
-                            GramPos_6 = "rifa",
-                            GramNeg_1 = "gent",
-                            GramNeg_2 = "tobr",
-                            GramNeg_3 = "coli",
-                            GramNeg_4 = "cfot",
-                            GramNeg_5 = "cfta",
-                            GramNeg_6 = "mero",
-                            warnings = TRUE,
-                            col_bactid = "bactid") {
-
-  if (col_bactid %in% colnames(tbl)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  }
-  if (!col_mo %in% colnames(tbl)) {
-    stop('Column ', col_mo, ' not found.', call. = FALSE)
-  }
-
-  # 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)
-  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = 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.na(gram_positive)]
-
-  gram_negative = c(universal,
-                    GramNeg_1, GramNeg_2, GramNeg_3,
-                    GramNeg_4, GramNeg_5, GramNeg_6)
-  gram_negative <- gram_negative[!is.na(gram_negative)]
-
-  if (!tbl %>% pull(col_mo) %>% is.mo()) {
-    tbl[, col_mo] <- as.mo(tbl[, col_mo])
-  }
-  # join microorganisms
-  tbl <- tbl %>% left_join_microorganisms(col_mo)
-
-  tbl$key_ab <- NA_character_
-
-  # Gram +
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain == "Gram positive",
-                                  apply(X = tbl[, gram_positive],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-
-  # Gram -
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain == "Gram negative",
-                                  apply(X = tbl[, gram_negative],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-
-  # format
-  key_abs <- tbl %>%
-    pull(key_ab) %>%
-    gsub('(NA|NULL)', '.', .) %>%
-    gsub('[^SIR]', '.', ., ignore.case = TRUE)
-
-  key_abs
-
-}
-
-#' @importFrom dplyr progress_estimated %>%
-#' @rdname key_antibiotics
-#' @export
-key_antibiotics_equal <- function(x,
-                                  y,
-                                  type = c("keyantibiotics", "points"),
-                                  ignore_I = TRUE,
-                                  points_threshold = 2,
-                                  info = FALSE) {
-  # x is active row, y is lag
-
-  type <- type[1]
-
-  if (length(x) != length(y)) {
-    stop('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 <- dplyr::progress_estimated(length(x))
-  }
-
-  for (i in 1:length(x)) {
-
-    if (info_needed == TRUE) {
-      p$tick()$print()
-    }
-
-    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 %>% as.rsi() %>% as.double())
-        suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
-
-        points <- (x_split - y_split) %>% abs() %>% 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 ?first_isolate.')
-      }
-    }
-  }
-  if (info_needed == TRUE) {
-    cat('\n')
-  }
-  result
-}
@@ -1,40 +0,0 @@
-#' Kurtosis of the sample
-#'
-#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
-#'
-#' @param x a vector of values, a \code{matrix} or a \code{data frame}
-#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
-#' @exportMethod kurtosis
-#' @seealso \code{\link{skewness}}
-#' @rdname kurtosis
-#' @export
-kurtosis <- function(x, na.rm = FALSE) {
-  UseMethod("kurtosis")
-}
-
-#' @exportMethod kurtosis.default
-#' @rdname kurtosis
-#' @export
-kurtosis.default <- function (x, na.rm = FALSE) {
-  x <- as.vector(x)
-  if (na.rm == TRUE) {
-    x <- x[!is.na(x)]
-  }
-  n <- length(x)
-  n * base::sum((x - base::mean(x, na.rm = na.rm))^4, na.rm = na.rm) /
-    (base::sum((x - base::mean(x, na.rm = na.rm))^2, na.rm = na.rm)^2)
-}
-
-#' @exportMethod kurtosis.matrix
-#' @rdname kurtosis
-#' @export
-kurtosis.matrix <- function (x, na.rm = FALSE) {
-  base::apply(x, 2, kurtosis.default, na.rm = na.rm)
-}
-
-#' @exportMethod kurtosis.data.frame
-#' @rdname kurtosis
-#' @export
-kurtosis.data.frame <- function (x, na.rm = FALSE) {
-  base::sapply(x, kurtosis.default, na.rm = na.rm)
-}
@@ -1,80 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Pattern Matching
-#'
-#' Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors.
-#' @inheritParams base::grepl
-#' @return A \code{logical} vector
-#' @name like
-#' @rdname like
-#' @export
-#' @details Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
-#' @source Idea from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default and let it support multiple patterns.
-#' @seealso \code{\link[base]{grep}}
-#' @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
-#' a <- c("Test case", "Something different", "Yet another thing")
-#' b <- c("case", "diff", "yet")
-#' a %like% b
-#' #> TRUE TRUE TRUE
-#'
-#' # get frequencies of bacteria whose name start with 'Ent' or 'ent'
-#' library(dplyr)
-#' septic_patients %>%
-#'   left_join_microorganisms() %>%
-#'   filter(genus %like% '^ent') %>%
-#'   freq(genus, species)
-like <- function(x, pattern) {
-  if (length(pattern) > 1) {
-    if (length(x) != length(pattern)) {
-      pattern <- pattern[1]
-      warning('only the first element of argument `pattern` used for `%like%`', call. = FALSE)
-    } else {
-      # x and pattern are of same length, so items with each other
-      res <- vector(length = length(pattern))
-      for (i in 1:length(res)) {
-        if (is.factor(x[i])) {
-          res[i] <- as.integer(x[i]) %in% base::grep(pattern[i], levels(x[i]), ignore.case = TRUE)
-        } else {
-          res[i] <- base::grepl(pattern[i], x[i], ignore.case = TRUE)
-        }
-      }
-      return(res)
-    }
-  }
-
-  # the regular way how grepl works; just one pattern against one or more x
-  if (is.factor(x)) {
-    as.integer(x) %in% base::grep(pattern, levels(x), ignore.case = TRUE)
-  } else {
-    base::grepl(pattern, x, ignore.case = TRUE)
-  }
-}
-
-#' @rdname like
-#' @export
-"%like%" <- like
@@ -1,433 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Determine multidrug-resistant organisms (MDRO)
-#'
-#' Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
-#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
-#' @param country country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).
-#' @param info print progress
-#' @inheritParams eucast_rules
-#' @param metr column name of an antibiotic. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing columns will anyway be skipped. See the Antibiotics section for an explanation of the abbreviations.
-#' @param ... parameters that are passed on to methods
-#' @inheritSection eucast_rules Antibiotics
-#' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}).
-#' @return Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
-#' @rdname mdro
-#' @importFrom dplyr %>%
-#' @importFrom crayon red blue
-#' @export
-#' @examples
-#' library(dplyr)
-#'
-#' septic_patients %>%
-#'   mutate(EUCAST = mdro(.),
-#'          BRMO = brmo(.))
-mdro <- function(tbl,
-                 country = NULL,
-                 col_mo = NULL,
-                 info = TRUE,
-                 amcl = 'amcl',
-                 amik = 'amik',
-                 amox = 'amox',
-                 ampi = 'ampi',
-                 azit = 'azit',
-                 aztr = 'aztr',
-                 cefa = 'cefa',
-                 cfra = 'cfra',
-                 cfep = 'cfep',
-                 cfot = 'cfot',
-                 cfox = 'cfox',
-                 cfta = 'cfta',
-                 cftr = 'cftr',
-                 cfur = 'cfur',
-                 chlo = 'chlo',
-                 cipr = 'cipr',
-                 clar = 'clar',
-                 clin = 'clin',
-                 clox = 'clox',
-                 coli = 'coli',
-                 czol = 'czol',
-                 dapt = 'dapt',
-                 doxy = 'doxy',
-                 erta = 'erta',
-                 eryt = 'eryt',
-                 fosf = 'fosf',
-                 fusi = 'fusi',
-                 gent = 'gent',
-                 imip = 'imip',
-                 kana = 'kana',
-                 levo = 'levo',
-                 linc = 'linc',
-                 line = 'line',
-                 mero = 'mero',
-                 metr = 'metr',
-                 mino = 'mino',
-                 moxi = 'moxi',
-                 nali = 'nali',
-                 neom = 'neom',
-                 neti = 'neti',
-                 nitr = 'nitr',
-                 novo = 'novo',
-                 norf = 'norf',
-                 oflo = 'oflo',
-                 peni = 'peni',
-                 pipe = 'pipe',
-                 pita = 'pita',
-                 poly = 'poly',
-                 qida = 'qida',
-                 rifa = 'rifa',
-                 roxi = 'roxi',
-                 siso = 'siso',
-                 teic = 'teic',
-                 tetr = 'tetr',
-                 tica = 'tica',
-                 tige = 'tige',
-                 tobr = 'tobr',
-                 trim = 'trim',
-                 trsu = 'trsu',
-                 vanc = 'vanc',
-                 col_bactid = NULL) {
-
-  if (!is.data.frame(tbl)) {
-    stop("`tbl` must be a data frame.", call. = FALSE)
-  }
-
-  # try to find columns based on type
-  # -- mo
-  if (!is.null(col_bactid)) {
-    col_mo <- col_bactid
-    warning("Use of `col_bactid` is deprecated. Use `col_mo` instead.")
-  } else if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
-    message("NOTE: Using column `", col_mo, "` as input for `col_mo`.")
-  } else if (!col_mo %in% colnames(tbl)) {
-    stop('Column ', col_mo, ' not found.', call. = FALSE)
-  }
-
-  # strip whitespaces
-  if (length(country) > 1) {
-    stop('`country` must be a length one character string.', call. = FALSE)
-  }
-
-  if (is.null(country)) {
-    country <- 'EUCAST'
-  }
-  country <- trimws(country)
-  if (tolower(country) != 'eucast' & !country %like% '^[a-z]{2}$') {
-    stop('This is not a valid ISO 3166-1 alpha-2 country code: "', country, '". Please see ?mdro.', call. = FALSE)
-  }
-
-  # create list and make country code case-independent
-  guideline <- list(country = list(code = tolower(country)))
-
-  if (guideline$country$code == 'eucast') {
-    guideline$country$name <- '(European guidelines)'
-    guideline$name <- 'EUCAST Expert Rules, "Intrinsic Resistance and Exceptional Phenotypes Tables"'
-    guideline$version <- 'Version 3.1'
-    guideline$source <- 'http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'
-    # support per country:
-  } else if (guideline$country$code == 'de') {
-    guideline$country$name <- 'Germany'
-    guideline$name <- ''
-    guideline$version <- ''
-    guideline$source <- ''
-  } else if (guideline$country$code == 'nl') {
-    guideline$country$name <- 'The Netherlands'
-    guideline$name <- 'WIP-Richtlijn BRMO'
-    guideline$version <- 'Revision of December 2017'
-    guideline$source <- 'https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'
-    # add here more countries like this:
-    # } else if (country$code == 'xx') {
-    #   country$name <- 'country name'
-  } else {
-    stop('This country code is currently unsupported: ', guideline$country$code, call. = FALSE)
-  }
-
-  if (info == TRUE) {
-    cat("Determining multidrug-resistant organisms (MDRO), according to:\n",
-        "Guideline: ", red(paste0(guideline$name, ", ", guideline$version, "\n")),
-        "Country  : ", red(paste0(guideline$country$name, "\n")),
-        "Source   : ", blue(paste0(guideline$source, "\n")),
-        "\n", sep = "")
-  }
-
-  # check columns
-  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
-                cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
-                czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
-                levo, linc, line, mero, metr, mino, moxi, nali, neom, neti, nitr,
-                novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso,
-                teic, tetr, tica, tige, tobr, trim, trsu, vanc)
-  col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
-  amcl <- col.list[amcl]
-  amik <- col.list[amik]
-  amox <- col.list[amox]
-  ampi <- col.list[ampi]
-  azit <- col.list[azit]
-  aztr <- col.list[aztr]
-  cefa <- col.list[cefa]
-  cfra <- col.list[cfra]
-  cfep <- col.list[cfep]
-  cfot <- col.list[cfot]
-  cfox <- col.list[cfox]
-  cfta <- col.list[cfta]
-  cftr <- col.list[cftr]
-  cfur <- col.list[cfur]
-  chlo <- col.list[chlo]
-  cipr <- col.list[cipr]
-  clar <- col.list[clar]
-  clin <- col.list[clin]
-  clox <- col.list[clox]
-  coli <- col.list[coli]
-  czol <- col.list[czol]
-  dapt <- col.list[dapt]
-  doxy <- col.list[doxy]
-  erta <- col.list[erta]
-  eryt <- col.list[eryt]
-  fosf <- col.list[fosf]
-  fusi <- col.list[fusi]
-  gent <- col.list[gent]
-  imip <- col.list[imip]
-  kana <- col.list[kana]
-  levo <- col.list[levo]
-  linc <- col.list[linc]
-  line <- col.list[line]
-  mero <- col.list[mero]
-  metr <- col.list[metr]
-  mino <- col.list[mino]
-  moxi <- col.list[moxi]
-  nali <- col.list[nali]
-  neom <- col.list[neom]
-  neti <- col.list[neti]
-  nitr <- col.list[nitr]
-  novo <- col.list[novo]
-  norf <- col.list[norf]
-  oflo <- col.list[oflo]
-  peni <- col.list[peni]
-  pita <- col.list[pita]
-  poly <- col.list[poly]
-  qida <- col.list[qida]
-  rifa <- col.list[rifa]
-  roxi <- col.list[roxi]
-  siso <- col.list[siso]
-  teic <- col.list[teic]
-  tetr <- col.list[tetr]
-  tica <- col.list[tica]
-  tige <- col.list[tige]
-  tobr <- col.list[tobr]
-  trim <- col.list[trim]
-  trsu <- col.list[trsu]
-  vanc <- col.list[vanc]
-
-  # antibiotic classes
-  aminoglycosides <- c(tobr, gent) # can also be kana but that one is often intrinsic R
-  cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
-  cephalosporins_3rd <- c(cfot, cftr, cfta)
-  carbapenems <- c(erta, imip, mero)
-  fluoroquinolones <- c(oflo, cipr, levo, moxi)
-
-  # helper function for editing the table
-  trans_tbl <- function(to, rows, cols, any_all) {
-    cols <- cols[!is.na(cols)]
-    if (length(rows) > 0 & length(cols) > 0) {
-      if (any_all == "any") {
-        col_filter <- which(tbl[, cols] == 'R')
-      } else if (any_all == "all") {
-        col_filter <- tbl %>%
-          mutate(index = 1:nrow(.)) %>%
-          filter_at(vars(cols), all_vars(. == "R")) %>%
-          pull((index))
-      }
-      rows <- rows[rows %in% col_filter]
-      tbl[rows, 'MDRO'] <<- to
-    }
-  }
-
-  if (!tbl %>% pull(col_mo) %>% is.mo()) {
-    tbl[, col_mo] <- as.mo(tbl[, col_mo])
-  }
-
-  tbl <- tbl %>%
-    # join to microorganisms data set
-    left_join_microorganisms(by = col_mo) %>%
-    # add unconfirmed to where genus is available
-    mutate(MDRO = ifelse(!is.na(genus), 1, NA_integer_))
-
-  if (guideline$country$code == 'eucast') {
-    # EUCAST ------------------------------------------------------------------
-    # Table 5
-    trans_tbl(3,
-              which(tbl$family == 'Enterobacteriaceae'
-                    | tbl$fullname %like% '^Pseudomonas aeruginosa'
-                    | tbl$genus == 'Acinetobacter'),
-              coli,
-              "all")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Salmonella Typhi'),
-              c(carbapenems, fluoroquinolones),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Haemophilus influenzae'),
-              c(cephalosporins_3rd, carbapenems, fluoroquinolones),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Moraxella catarrhalis'),
-              c(cephalosporins_3rd, fluoroquinolones),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Neisseria meningitidis'),
-              c(cephalosporins_3rd, fluoroquinolones),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Neisseria gonorrhoeae'),
-              azit,
-              "any")
-    # Table 6
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
-              c(vanc, teic, dapt, line, qida, tige),
-              "any")
-    trans_tbl(3,
-              which(tbl$genus == 'Corynebacterium'),
-              c(vanc, teic, dapt, line, qida, tige),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Streptococcus pneumoniae'),
-              c(carbapenems, vanc, teic, dapt, line, qida, tige, rifa),
-              "any")
-    trans_tbl(3, # Sr. groups A/B/C/G
-              which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|equisimilis|equi|zooepidemicus|dysgalactiae|anginosus)'),
-              c(peni, cephalosporins, vanc, teic, dapt, line, qida, tige),
-              "any")
-    trans_tbl(3,
-              which(tbl$genus == 'Enterococcus'),
-              c(dapt, line, tige, teic),
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Enterococcus faecalis'),
-              c(ampi, amox),
-              "any")
-    # Table 7
-    trans_tbl(3,
-              which(tbl$genus == 'Bacteroides'),
-              metr,
-              "any")
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Clostridium difficile'),
-              c(metr, vanc),
-              "any")
-  }
-
-  if (guideline$country$code == 'de') {
-    # Germany -----------------------------------------------------------------
-    stop("We are still working on German guidelines in this beta version.", call. = FALSE)
-  }
-
-  if (guideline$country$code == 'nl') {
-    # Netherlands -------------------------------------------------------------
-    aminoglycosides <- aminoglycosides[!is.na(aminoglycosides)]
-    fluoroquinolones <- fluoroquinolones[!is.na(fluoroquinolones)]
-    carbapenems <- carbapenems[!is.na(carbapenems)]
-
-    # Table 1
-    trans_tbl(3,
-              which(tbl$family == 'Enterobacteriaceae'),
-              c(aminoglycosides, fluoroquinolones),
-              "all")
-
-    trans_tbl(2,
-              which(tbl$family == 'Enterobacteriaceae'),
-              c(carbapenems),
-              "any")
-
-    # Table 2
-    trans_tbl(2,
-              which(tbl$genus == 'Acinetobacter'),
-              c(carbapenems),
-              "any")
-    trans_tbl(3,
-              which(tbl$genus == 'Acinetobacter'),
-              c(aminoglycosides, fluoroquinolones),
-              "all")
-
-    trans_tbl(3,
-              which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
-              trsu,
-              "all")
-
-    if (!is.na(mero) & !is.na(imip)
-        & !is.na(gent) & !is.na(tobr)
-        & !is.na(cipr)
-        & !is.na(cfta)
-        & !is.na(pita) ) {
-      tbl <- tbl %>% mutate(
-        psae = 0,
-        psae = ifelse(mero == "R" | imip == "R", psae + 1, psae),
-        psae = ifelse(gent == "R" & tobr == "R", psae + 1, psae),
-        psae = ifelse(cipr == "R", psae + 1, psae),
-        psae = ifelse(cfta == "R", psae + 1, psae),
-        psae = ifelse(pita == "R", psae + 1, psae),
-        psae = ifelse(is.na(psae), 0, psae)
-      )
-    } else {
-      tbl$psae <- 0
-    }
-    tbl[which(
-      tbl$fullname %like% 'Pseudomonas aeruginosa'
-      & tbl$psae >= 3
-    ), 'MDRO'] <- 3
-
-    # Table 3
-    trans_tbl(3,
-              which(tbl$fullname %like% 'Streptococcus pneumoniae'),
-              peni,
-              "all")
-    trans_tbl(3,
-              which(tbl$fullname %like% 'Streptococcus pneumoniae'),
-              vanc,
-              "all")
-    trans_tbl(3,
-              which(tbl$fullname %like% 'Enterococcus faecium'),
-              c(peni, vanc),
-              "all")
-  }
-
-  factor(x =  tbl$MDRO,
-         levels = 1:3,
-         labels = c('Negative', 'Positive, unconfirmed', 'Positive'),
-         ordered = TRUE)
-}
-
-#' @rdname mdro
-#' @export
-brmo <- function(..., country = "nl") {
-  mdro(..., country = "nl")
-}
-
-#' @rdname mdro
-#' @export
-mrgn <- function(tbl, country = "de", ...) {
-  mdro(tbl = tbl, country = "de", ...)
-}
-
-#' @rdname mdro
-#' @export
-eucast_exceptional_phenotypes <- function(tbl, country = "EUCAST", ...) {
-  mdro(tbl = tbl, country = "EUCAST", ...)
-}
@@ -1,244 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Class 'mic'
-#'
-#' This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
-#' @rdname as.mic
-#' @param x vector
-#' @param na.rm a logical indicating whether missing values should be removed
-#' @return Ordered factor with new class \code{mic}
-#' @keywords mic
-#' @export
-#' @importFrom dplyr %>%
-#' @seealso \code{\link{as.rsi}}
-#' @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
-#'
-#' plot(mic_data)
-#' barplot(mic_data)
-#' freq(mic_data)
-as.mic <- function(x, na.rm = FALSE) {
-  if (is.mic(x)) {
-    x
-  } else {
-    x <- x %>% unlist()
-    if (na.rm == TRUE) {
-      x <- x[!is.na(x)]
-    }
-    x.bak <- x
-
-    # comma to period
-    x <- gsub(',', '.', x, fixed = TRUE)
-    # remove space between operator and number ("<= 0.002" -> "<=0.002")
-    x <- gsub('(<|=|>) +', '\\1', x)
-    # starting dots must start with 0
-    x <- gsub('^[.]+', '0.', x)
-    # <=0.2560.512 should be 0.512
-    x <- gsub('.*[.].*[.]', '0.', x)
-    # remove ending .0
-    x <- gsub('[.]+0$', '', x)
-    # remove all after last digit
-    x <- gsub('[^0-9]+$', '', x)
-    # remove last zeroes
-    x <- gsub('([.].?)0+$', '\\1', x)
-    # force to be character
-    x <- as.character(x)
-
-    # previously unempty values now empty - should return a warning later on
-    x[x.bak != "" & x == ""] <- "invalid"
-
-    # these are alllowed MIC values and will become factor levels
-    lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
-              "<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
-              "<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
-              "<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
-              "<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
-              "<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
-              "<0.0125", "<=0.0125", "0.0125", ">=0.0125", ">0.0125",
-              "<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
-              "<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
-              "<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
-              "<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
-              "<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
-              "<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
-              "<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
-              "<0.054", "<=0.054", "0.054", ">=0.054", ">0.054",
-              "<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
-              "<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
-              "<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
-              "<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
-              "<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
-              "<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
-              "<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
-              "<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
-              "<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
-              "<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
-              "<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
-              "<0.23", "<=0.23", "0.23", ">=0.23", ">0.23",
-              "<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
-              "<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
-              "<0.28", "<=0.28", "0.28", ">=0.28", ">0.28",
-              "<0.3", "<=0.3", "0.3", ">=0.3", ">0.3",
-              "<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
-              "<0.36", "<=0.36", "0.36", ">=0.36", ">0.36",
-              "<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
-              "<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
-              "<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
-              "<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
-              "<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
-              "<1", "<=1", "1", ">=1", ">1",
-              "<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
-              "<2", "<=2", "2", ">=2", ">2",
-              "<3", "<=3", "3", ">=3", ">3",
-              "<4", "<=4", "4", ">=4", ">4",
-              "<5", "<=5", "5", ">=5", ">5",
-              "<6", "<=6", "6", ">=6", ">6",
-              "<7", "<=7", "7", ">=7", ">7",
-              "<8", "<=8", "8", ">=8", ">8",
-              "<10", "<=10", "10", ">=10", ">10",
-              "<12", "<=12", "12", ">=12", ">12",
-              "<16", "<=16", "16", ">=16", ">16",
-              "<20", "<=20", "20", ">=20", ">20",
-              "<24", "<=24", "24", ">=24", ">24",
-              "<32", "<=32", "32", ">=32", ">32",
-              "<40", "<=40", "40", ">=40", ">40",
-              "<48", "<=48", "48", ">=48", ">48",
-              "<64", "<=64", "64", ">=64", ">64",
-              "<80", "<=80", "80", ">=80", ">80",
-              "<96", "<=96", "96", ">=96", ">96",
-              "<128", "<=128", "128", ">=128", ">128",
-              "<160", "<=160", "160", ">=160", ">160",
-              "<256", "<=256", "256", ">=256", ">256",
-              "<320", "<=320", "320", ">=320", ">320",
-              "<512", "<=512", "512", ">=512", ">512",
-              "<1024", "<=1024", "1024", ">=1024", ">1024")
-
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    x[!x %in% lvls] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        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)
-    }
-
-    x <- factor(x, levels = lvls, ordered = TRUE)
-    class(x) <- c('mic', 'ordered', 'factor')
-    x
-  }
-}
-
-#' @rdname as.mic
-#' @export
-#' @importFrom dplyr %>%
-is.mic <- function(x) {
-  class(x) %>% identical(c('mic', 'ordered', 'factor'))
-}
-
-#' @exportMethod as.double.mic
-#' @export
-#' @noRd
-as.double.mic <- function(x, ...) {
-  as.double(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.integer.mic
-#' @export
-#' @noRd
-as.integer.mic <- function(x, ...) {
-  as.integer(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod as.numeric.mic
-#' @export
-#' @noRd
-as.numeric.mic <- function(x, ...) {
-  as.numeric(gsub('(<|=|>)+', '', as.character(x)))
-}
-
-#' @exportMethod print.mic
-#' @export
-#' @importFrom dplyr %>% tibble group_by summarise pull
-#' @noRd
-print.mic <- function(x, ...) {
-  cat("Class 'mic'\n")
-  print(as.character(x), quote = FALSE)
-}
-
-#' @exportMethod summary.mic
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  lst <- c('mic',
-           n_total - n,
-           sort(x)[1] %>% as.character(),
-           sort(x)[n] %>% as.character())
-  names(lst) <- c("Mode", "<NA>", "Min.", "Max.")
-  lst
-}
-
-#' @exportMethod plot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics plot text
-#' @noRd
-plot.mic <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-  create_barplot_mic(x, x_name, ...)
-}
-
-#' @exportMethod barplot.mic
-#' @export
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.mic <- function(height, ...) {
-  x_name <- deparse(substitute(height))
-  create_barplot_mic(height, x_name, ...)
-}
-
-#' @importFrom graphics barplot axis
-#' @importFrom dplyr %>% group_by summarise
-create_barplot_mic <- function(x, x_name, ...) {
-  data <- data.frame(mic = x, cnt = 1) %>%
-    group_by(mic) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-  barplot(table(droplevels(x)),
-          ylab = 'Frequency',
-          xlab = 'MIC value',
-          main = paste('MIC values of', x_name),
-          axes = FALSE,
-          ...)
-  axis(2, seq(0, max(data$cnt)))
-}
@@ -1,106 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-# No export, no Rd
-addin_insert_in <- function() {
-  rstudioapi::insertText(" %in% ")
-}
-
-# No export, no Rd
-addin_insert_like <- function() {
-  rstudioapi::insertText(" %like% ")
-}
-
-# No export, no Rd
-percent <- function(x, round = 1, force_zero = FALSE, ...) {
-
-  # https://stackoverflow.com/a/12688836/4575331
-  round2 <- function(x, n) (trunc((abs(x) * 10 ^ n) + 0.5) / 10 ^ n) * sign(x)
-
-  val <- round2(x, round + 2) # round up 0.5
-  val <- round(x = val * 100, digits = round) # remove floating point error
-
-  if (force_zero == TRUE) {
-    if (any(val == as.integer(val) & !is.na(val))) {
-      # add zeroes to all integers
-      val[val == as.integer(as.character(val))] <- paste0(val[val == as.integer(val)], ".", strrep(0, round))
-    }
-    # add extra zeroes if needed
-    val_decimals <- nchar(gsub(".*[.](.*)", "\\1", as.character(val)))
-    val[val_decimals < round] <- paste0(val[val_decimals < round], strrep(0, max(0, round - val_decimals)))
-  }
-  pct <- base::paste0(val, "%")
-  pct[pct %in% c("NA%", "NaN%")] <- NA_character_
-  pct
-}
-
-check_available_columns <- function(tbl, col.list, info = TRUE) {
-  # check columns
-  col.list <- col.list[!is.na(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 1:length(col.list)) {
-    if (toupper(col.list[i]) %in% colnames(tbl)) {
-      col.list[i] <- toupper(col.list[i])
-    } else if (tolower(col.list[i]) %in% colnames(tbl)) {
-      col.list[i] <- tolower(col.list[i])
-    } else if (!col.list[i] %in% colnames(tbl)) {
-      col.list[i] <- NA
-    }
-  }
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored: ',
-              col.list.bak[!(col.list %in% colnames(tbl))] %>% toString(),
-              '.\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.',
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-  }
-  col.list
-}
-
-# Coefficient of variation (CV)
-cv <- function(x, na.rm = TRUE) {
-  stats::sd(x, na.rm = na.rm) / base::abs(base::mean(x, na.rm = na.rm))
-}
-
-# Coefficient of dispersion, or coefficient of quartile variation (CQV).
-# (Bonett et al., 2006: Confidence interval for a coefficient of quartile variation).
-cqv <- function(x, na.rm = TRUE) {
-  fives <- stats::fivenum(x, na.rm = na.rm)
-  (fives[4] - fives[2]) / (fives[4] + fives[2])
-}
-
-# show bytes as kB/MB/GB
-# size_humanreadable(123456) # 121 kB
-# size_humanreadable(12345678) # 11.8 MB
-size_humanreadable <- function(bytes, decimals = 1) {
-  bytes <- bytes %>% as.double()
-  # Adapted from:
-  # http://jeffreysambells.com/2012/10/25/human-readable-filesize-php
-  size <- c('B','kB','MB','GB','TB','PB','EB','ZB','YB')
-  factor <- floor((nchar(bytes) - 1) / 3)
-  # added slight improvement; no decimals for B and kB:
-  decimals <- rep(decimals, length(bytes))
-  decimals[size[factor + 1] %in% c('B', 'kB')] <- 0
-
-  out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
-  out
-}
@@ -1,735 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Transform to microorganism ID
-#'
-#' Use this function to determine a valid microorganism ID (\code{mo}). Determination is done using Artificial Intelligence (AI) and the complete taxonomic kingdoms \emph{Bacteria}, \emph{Fungi} and \emph{Protozoa} (see Source), so the input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), an abbreviation known in the field (like \code{"MRSA"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
-#' @param x a character vector or a \code{data.frame} with one or two columns
-#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1].
-#'
-#'   This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".
-#' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, e.g. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L.
-#'
-#'   This excludes \emph{Enterococci} at default (who are in group D), use \code{Lancefield = "all"} to also categorise all \emph{Enterococci} as group D.
-#' @param allow_uncertain a logical to indicate whether empty results should be checked for only a part of the input string. When results are found, a warning will be given about the uncertainty and the result.
-#' @param reference_df a \code{data.frame} to use for extra reference when translating \code{x} to a valid \code{mo}. The first column can be any microbial name, code or ID (used in your analysis or organisation), the second column must be a valid \code{mo} as found in the \code{\link{microorganisms}} data set.
-#' @rdname as.mo
-#' @aliases mo
-#' @keywords mo Becker becker Lancefield lancefield guess
-#' @details
-#' A microbial ID from this package (class: \code{mo}) typically looks like these examples:\cr
-#' \preformatted{
-#'   Code                Full name
-#'   ---------------     --------------------------------------
-#'   B_KLBSL             Klebsiella
-#'   B_KLBSL_PNE         Klebsiella pneumoniae
-#'   B_KLBSL_PNE_RHI     Klebsiella pneumoniae rhinoscleromatis
-#'   |   |    |   |
-#'   |   |    |   |
-#'   |   |    |    ----> subspecies, a 3-4 letter acronym
-#'   |   |     ----> species, a 3-4 letter acronym
-#'   |    ----> genus, a 5-7 letter acronym, mostly without vowels
-#'    ----> taxonomic kingdom, either B (Bacteria), F (Fungi) or P (Protozoa)
-#' }
-#'
-#' Use the \code{\link{mo_property}} functions to get properties based on the returned code, see Examples.
-#'
-#' This function uses Artificial Intelligence (AI) to help getting fast and logical results. It tries to find matches in this order:
-#' \itemize{
-#'   \item{Taxonomic kingdom: it first searches in bacteria, then fungi, then protozoa}
-#'   \item{Human pathogenic prevalence: it first searches in more prevalent microorganisms, then less prevalent ones}
-#'   \item{Valid MO codes and full names: it first searches in already valid MO code and known genus/species combinations}
-#'   \item{Breakdown of input values: from here it starts to breakdown input values to find possible matches}
-#' }
-#'
-#' A couple of effects because of these rules
-#' \itemize{
-#'   \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
-#'   \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae} for the same reason}
-#'   \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
-#'   \item{Something like \code{"stau"} or \code{"S aur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
-#' }
-#' This means that looking up human pathogenic microorganisms takes less time than looking up human \strong{non}-pathogenic microorganisms.
-#'
-#' \code{guess_mo} is an alias of \code{as.mo}.
-#' @section ITIS:
-#' \if{html}{\figure{itis_logo.jpg}{options: height=60px style=margin-bottom:5px} \cr}
-#' This package contains the \strong{complete microbial taxonomic data} (with all eight taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, \url{https://www.itis.gov}).
-#'
-#' All (sub)species from \strong{the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package}, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
-#'
-#' ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists [3].
-#'
-#  (source as a section, so it can be inherited by other man pages)
-#' @section Source:
-#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \url{https://dx.doi.org/10.1128/CMR.00109-13}
-#'
-#' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \url{https://dx.doi.org/10.1084/jem.57.4.571}
-#'
-#' [3] Integrated Taxonomic Information System (ITIS). Retrieved September 2018. \url{http://www.itis.gov}
-#' @export
-#' @return Character (vector) with class \code{"mo"}. Unknown values will return \code{NA}.
-#' @seealso \code{\link{microorganisms}} for the \code{data.frame} with ITIS content that is being used to determine ID's. \cr
-#' The \code{\link{mo_property}} functions (like \code{\link{mo_genus}}, \code{\link{mo_gramstain}}) to get properties based on the returned code.
-#' @examples
-#' # These examples all return "B_STPHY_AUR", the ID of S. aureus:
-#' as.mo("stau")
-#' as.mo("STAU")
-#' as.mo("staaur")
-#' as.mo("S. aureus")
-#' as.mo("S aureus")
-#' as.mo("Staphylococcus aureus")
-#' as.mo("MRSA") # Methicillin Resistant S. aureus
-#' as.mo("VISA") # Vancomycin Intermediate S. aureus
-#' as.mo("VRSA") # Vancomycin Resistant S. aureus
-#' as.mo(369)    # Search on TSN (Taxonomic Serial Number), a unique identifier
-#'               # for the Integrated Taxonomic Information System (ITIS)
-#'
-#' as.mo("Streptococcus group A")
-#' as.mo("GAS") # Group A Streptococci
-#' as.mo("GBS") # Group B Streptococci
-#'
-#' # guess_mo is an alias of as.mo and works the same
-#' guess_mo("S. epidermidis")                 # will remain species: B_STPHY_EPI
-#' guess_mo("S. epidermidis", Becker = TRUE)  # will not remain species: B_STPHY_CNS
-#'
-#' guess_mo("S. pyogenes")                    # will remain species: B_STRPTC_PYO
-#' guess_mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPTC_GRA
-#'
-#' # Use mo_* functions to get a specific property based on `mo`
-#' Ecoli <- as.mo("E. coli")     # returns `B_ESCHR_COL`
-#' mo_genus(Ecoli)               # returns "Escherichia"
-#' mo_gramstain(Ecoli)           # returns "Gram negative"
-#' # but it uses as.mo internally too, so you could also just use:
-#' mo_genus("E. coli")           # returns "Escherichia"
-#'
-#'
-#' \dontrun{
-#' df$mo <- as.mo(df$microorganism_name)
-#'
-#' # the select function of tidyverse is also supported:
-#' library(dplyr)
-#' df$mo <- df %>%
-#'   select(microorganism_name) %>%
-#'   as.mo()
-#'
-#' # and can even contain 2 columns, which is convenient for genus/species combinations:
-#' df$mo <- df %>%
-#'   select(genus, species) %>%
-#'   as.mo()
-#' # although this works easier and does the same:
-#' df <- df %>%
-#'   mutate(mo = as.mo(paste(genus, species)))
-#' }
-as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = FALSE, reference_df = NULL) {
-  structure(mo_validate(x = x, property = "mo",
-                        Becker = Becker, Lancefield = Lancefield,
-                        allow_uncertain = allow_uncertain, reference_df = reference_df),
-            class = "mo")
-}
-
-#' @rdname as.mo
-#' @export
-is.mo <- function(x) {
-  # bactid for older releases
-  # remove when is.bactid will be removed
-  identical(class(x), "mo") | identical(class(x), "bactid")
-}
-
-#' @rdname as.mo
-#' @export
-guess_mo <- as.mo
-
-#' @importFrom dplyr %>% pull left_join
-#' @importFrom data.table data.table as.data.table setkey
-exec_as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, allow_uncertain = FALSE, reference_df = NULL, property = "mo") {
-
-  if (!"AMR" %in% base::.packages()) {
-    library("AMR")
-    # These data.tables are available as data sets when the AMR package is loaded:
-    #   microorganismsDT        # this one is sorted by kingdom (B<F<P), prevalence, TSN
-    #   microorganisms.prevDT   # same as microorganismsDT, but with prevalence != 9999
-    #   microorganisms.unprevDT # same as microorganismsDT, but with prevalence == 9999
-    #   microorganisms.oldDT    # old taxonomic names, sorted by name (genus+species), TSN
-  }
-
-  if (NCOL(x) == 2) {
-    # support tidyverse selection like: df %>% select(colA, colB)
-    # paste these columns together
-    x_vector <- vector("character", NROW(x))
-    for (i in 1:NROW(x)) {
-      x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ")
-    }
-    x <- x_vector
-  } else {
-    if (NCOL(x) > 2) {
-      stop('`x` can be 2 columns at most', call. = FALSE)
-    }
-    x[is.null(x)] <- NA
-
-    # support tidyverse selection like: df %>% select(colA)
-    if (!is.vector(x) & !is.null(dim(x))) {
-      x <- pull(x, 1)
-    }
-  }
-
-  failures <- character(0)
-  x_input <- x
-  # only check the uniques, which is way faster
-  x <- unique(x)
-  # remove empty values (to later fill them in again with NAs)
-  x <- x[!is.na(x) & !is.null(x) & !identical(x, "")]
-
-  # defined df to check for
-  if (!is.null(reference_df)) {
-    if (!is.data.frame(reference_df) | NCOL(reference_df) < 2) {
-      stop('`reference_df` must be a data.frame with at least two columns.', call. = FALSE)
-    }
-    # remove factors, just keep characters
-    suppressWarnings(
-      reference_df[] <- lapply(reference_df, as.character)
-    )
-  }
-
-  if (all(x %in% microorganismsDT[["mo"]])) {
-    # existing mo codes when not looking for property "mo", like mo_genus("B_ESCHR_COL")
-    x <- microorganismsDT[data.table(mo = x), on = "mo", ..property][[1]]
-  } else if (!is.null(reference_df)
-             & all(x %in% reference_df[, 1])
-             & all(reference_df[, 2] %in% microorganismsDT[["mo"]])) {
-    # manually defined reference
-    colnames(reference_df)[1] <- "x"
-    colnames(reference_df)[2] <- "mo"
-    suppressWarnings(
-      x <- data.frame(x = x, stringsAsFactors = FALSE) %>%
-        left_join(reference_df, by = "x") %>%
-        left_join(microorganisms, by = "mo") %>%
-        pull(property)
-    )
-  } else if (all(toupper(x) %in% microorganisms.certe[, "certe"])) {
-    # old Certe codes
-    y <- as.data.table(microorganisms.certe)[data.table(certe = toupper(x)), on = "certe", ]
-    x <- microorganismsDT[data.table(mo = y[["mo"]]), on = "mo", ..property][[1]]
-
-  } else if (!all(x %in% microorganismsDT[[property]])) {
-
-    x_backup <- trimws(x, which = "both")
-
-    # remove spp and species
-    x <- gsub(" +(spp.?|species)", "", x_backup)
-    x_species <- paste(x, "species")
-    # translate to English for supported languages of mo_property
-    x <- gsub("(Gruppe|gruppe|groep|grupo|gruppo|groupe)", "group", x)
-    # remove 'empty' genus and species values
-    x <- gsub("(no MO)", "", x, fixed = TRUE)
-    # remove non-text in case of "E. coli" except dots and spaces
-    x <- gsub("[^.a-zA-Z0-9/ \\-]+", "", x)
-
-    # but spaces before and after should be omitted
-    x <- trimws(x, which = "both")
-    x_trimmed <- x
-    x_trimmed_species <- paste(x_trimmed, "species")
-    # replace space and dot by regex sign
-    x_withspaces <- gsub("[ .]+", ".* ", x)
-    x <- gsub("[ .]+", ".*", x)
-    # add start en stop regex
-    x <- paste0('^', x, '$')
-    x_withspaces_start <- paste0('^', x_withspaces)
-    x_withspaces <- paste0('^', x_withspaces, '$')
-
-    # cat(paste0('x                  "', x, '"\n'))
-    # cat(paste0('x_species          "', x_species, '"\n'))
-    # cat(paste0('x_withspaces_start "', x_withspaces_start, '"\n'))
-    # cat(paste0('x_withspaces       "', x_withspaces, '"\n'))
-    # cat(paste0('x_backup           "', x_backup, '"\n'))
-    # cat(paste0('x_trimmed          "', x_trimmed, '"\n'))
-    # cat(paste0('x_trimmed_species  "', x_trimmed_species, '"\n'))
-
-    for (i in 1:length(x)) {
-      if (identical(x_trimmed[i], "")) {
-        # empty values
-        x[i] <- NA_character_
-        next
-      }
-      if (nchar(x_trimmed[i]) < 3) {
-        # check if search term was like "A. species", then return first genus found with ^A
-        if (x_backup[i] %like% "species" | x_backup[i] %like% "spp[.]?") {
-          # get mo code of first hit
-          found <- microorganismsDT[fullname %like% x_withspaces_start[i], mo]
-          if (length(found) > 0) {
-            mo_code <- found[1L] %>% strsplit("_") %>% unlist() %>% .[1:2] %>% paste(collapse = "_")
-            found <- microorganismsDT[mo == mo_code, ..property][[1]]
-            # return first genus that begins with x_trimmed, e.g. when "E. spp."
-            if (length(found) > 0) {
-              x[i] <- found[1L]
-              next
-            }
-          }
-        }
-        # fewer than 3 chars and not looked for species, add as failure
-        x[i] <- NA_character_
-        failures <- c(failures, x_backup[i])
-        next
-      }
-
-
-      # translate known trivial abbreviations to genus + species ----
-      if (!is.na(x_trimmed[i])) {
-        if (toupper(x_trimmed[i]) == 'MRSA'
-            | toupper(x_trimmed[i]) == 'MSSA'
-            | toupper(x_trimmed[i]) == 'VISA'
-            | toupper(x_trimmed[i]) == 'VRSA') {
-          x[i] <- microorganismsDT[mo == 'B_STPHY_AUR', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) == 'MRSE'
-            | toupper(x_trimmed[i]) == 'MSSE') {
-          x[i] <- microorganismsDT[mo == 'B_STPHY_EPI', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) == 'VRE') {
-          x[i] <- microorganismsDT[mo == 'B_ENTRC', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) == 'MRPA') {
-          # multi resistant P. aeruginosa
-          x[i] <- microorganismsDT[mo == 'B_PDMNS_AER', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) == 'CRS'
-            | toupper(x_trimmed[i]) == 'CRSM') {
-          # co-trim resistant S. maltophilia
-          x[i] <- microorganismsDT[mo == 'B_STNTR_MAL', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) {
-          # peni I, peni R, vanco I, vanco R: S. pneumoniae
-          x[i] <- microorganismsDT[mo == 'B_STRPTC_PNE', ..property][[1]][1L]
-          next
-        }
-        if (toupper(x_trimmed[i]) %like% '^G[ABCDFGHK]S$') {
-          x[i] <- microorganismsDT[mo == gsub("G([ABCDFGHK])S", "B_STRPTC_GR\\1", x_trimmed[i]), ..property][[1]][1L]
-          next
-        }
-        # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) ----
-        if (tolower(x[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
-            | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] negatie?[vf]'
-            | tolower(x[i]) %like% '[ck]o?ns[^a-z]?$') {
-          # coerce S. coagulase negative
-          x[i] <- microorganismsDT[mo == 'B_STPHY_CNS', ..property][[1]][1L]
-          next
-        }
-        if (tolower(x[i]) %like% '[ck]oagulas[ea] positie?[vf]'
-            | tolower(x_trimmed[i]) %like% '[ck]oagulas[ea] positie?[vf]'
-            | tolower(x[i]) %like% '[ck]o?ps[^a-z]?$') {
-          # coerce S. coagulase positive
-          x[i] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L]
-          next
-        }
-        if (tolower(x[i]) %like% '^gram[ -]+nega.*'
-            | tolower(x_trimmed[i]) %like% '^gram[ -]+nega.*') {
-          # coerce S. coagulase positive
-          x[i] <- microorganismsDT[mo == 'B_GRAMN', ..property][[1]][1L]
-          next
-        }
-        if (tolower(x[i]) %like% '^gram[ -]+posi.*'
-            | tolower(x_trimmed[i]) %like% '^gram[ -]+posi.*') {
-          # coerce S. coagulase positive
-          x[i] <- microorganismsDT[mo == 'B_GRAMP', ..property][[1]][1L]
-          next
-        }
-      }
-
-      # FIRST TRY FULLNAMES AND CODES
-      # if only genus is available, return only genus
-      if (all(!c(x[i], x_trimmed[i]) %like% " ")) {
-        found <- microorganismsDT[tolower(fullname) %in% tolower(c(x_species[i], x_trimmed_species[i])), ..property][[1]]
-        if (length(found) > 0) {
-          x[i] <- found[1L]
-          next
-        }
-        if (nchar(x_trimmed[i]) > 4) {
-          # not when abbr is esco, stau, klpn, etc.
-          found <- microorganismsDT[tolower(fullname) %like% gsub(" ", ".*", x_trimmed_species[i], fixed = TRUE), ..property][[1]]
-          if (length(found) > 0) {
-            x[i] <- found[1L]
-            next
-          }
-        }
-      }
-
-      # TRY OTHER SOURCES ----
-      if (toupper(x_backup[i]) %in% microorganisms.certe[, 1]) {
-        mo_found <- microorganisms.certe[toupper(x_backup[i]) == microorganisms.certe[, 1], 2][1L]
-        if (length(mo_found) > 0) {
-          x[i] <- microorganismsDT[mo == mo_found, ..property][[1]][1L]
-          next
-        }
-      }
-      if (x_backup[i] %in% microorganisms.umcg[, 1]) {
-        mo_umcg <- microorganisms.umcg[microorganisms.umcg[, 1] == x_backup[i], 2]
-        mo_found <- microorganisms.certe[microorganisms.certe[, 1] == mo_umcg, 2][1L]
-        if (length(mo_found) == 0) {
-          # not found
-          x[i] <- NA_character_
-          failures <- c(failures, x_backup[i])
-        } else {
-          x[i] <- microorganismsDT[mo == mo_found, ..property][[1]][1L]
-        }
-        next
-      }
-      if (!is.null(reference_df)) {
-        if (x_backup[i] %in% reference_df[, 1]) {
-          ref_mo <- reference_df[reference_df[, 1] == x_backup[i], 2]
-          if (ref_mo %in% microorganismsDT[, mo]) {
-            x[i] <- microorganismsDT[mo == ref_mo, ..property][[1]][1L]
-            next
-          } else {
-            warning("Value '", x_backup[i], "' was found in reference_df, but '", ref_mo, "' is not a valid MO code.", call. = FALSE)
-          }
-        }
-      }
-
-      # TRY FIRST THOUSAND MOST PREVALENT IN HUMAN INFECTIONS ----
-      found <- microorganisms.prevDT[tolower(fullname) %in% tolower(c(x_backup[i], x_trimmed[i])), ..property][[1]]
-      # most probable: is exact match in fullname
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-      found <- microorganisms.prevDT[tsn == x_trimmed[i], ..property][[1]]
-      # is a valid TSN
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-      found <- microorganisms.prevDT[mo == toupper(x_backup[i]), ..property][[1]]
-      # is a valid mo
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match keeping spaces ----
-      found <- microorganisms.prevDT[fullname %like% x_withspaces[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match keeping spaces, not ending with $ ----
-      found <- microorganisms.prevDT[fullname %like% x_withspaces_start[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match diregarding spaces ----
-      found <- microorganisms.prevDT[fullname %like% x[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-
-      # try splitting of characters in the middle and then find ID ----
-      # only when text length is 6 or lower
-      # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus, staaur = S. aureus
-      if (nchar(x_trimmed[i]) <= 6) {
-        x_length <- nchar(x_trimmed[i])
-        x[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2),
-                       '.* ',
-                       x_trimmed[i] %>% substr((x_length / 2) + 1, x_length))
-        found <- microorganisms.prevDT[fullname %like% paste0('^', x[i]), ..property][[1]]
-        if (length(found) > 0) {
-          x[i] <- found[1L]
-          next
-        }
-      }
-
-      # try fullname without start and stop regex, to also find subspecies ----
-      # like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH
-      found <- microorganisms.prevDT[fullname %like% x_withspaces_start[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # THEN TRY ALL OTHERS ----
-      found <- microorganisms.unprevDT[tolower(fullname) == tolower(x_backup[i]), ..property][[1]]
-      # most probable: is exact match in fullname
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-      found <- microorganisms.unprevDT[tolower(fullname) == tolower(x_trimmed[i]), ..property][[1]]
-      # most probable: is exact match in fullname
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-      found <- microorganisms.unprevDT[tsn == x_trimmed[i], ..property][[1]]
-      # is a valid TSN
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-      found <- microorganisms.unprevDT[mo == toupper(x_backup[i]), ..property][[1]]
-      # is a valid mo
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match keeping spaces ----
-      found <- microorganisms.unprevDT[fullname %like% x_withspaces[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match keeping spaces, not ending with $ ----
-      found <- microorganisms.unprevDT[fullname %like% x_withspaces_start[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try any match diregarding spaces ----
-      found <- microorganisms.unprevDT[fullname %like% x[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # try splitting of characters in the middle and then find ID ----
-      # only when text length is 6 or lower
-      # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus, staaur = S. aureus
-      if (nchar(x_trimmed[i]) <= 6) {
-        x_length <- nchar(x_trimmed[i])
-        x[i] <- paste0(x_trimmed[i] %>% substr(1, x_length / 2),
-                       '.* ',
-                       x_trimmed[i] %>% substr((x_length / 2) + 1, x_length))
-        found <- microorganisms.unprevDT[fullname %like% paste0('^', x[i]), ..property][[1]]
-        if (length(found) > 0) {
-          x[i] <- found[1L]
-          next
-        }
-      }
-
-      # try fullname without start and stop regex, to also find subspecies ----
-      # like "K. pneu rhino" -> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH
-      found <- microorganisms.unprevDT[fullname %like% x_withspaces_start[i], ..property][[1]]
-      if (length(found) > 0) {
-        x[i] <- found[1L]
-        next
-      }
-
-      # MISCELLANEOUS ----
-
-      # look for old taxonomic names ----
-      found <- microorganisms.oldDT[tolower(name) == tolower(x_backup[i])
-                                    | tsn == x_trimmed[i]
-                                    | name %like% x_withspaces[i],]
-      if (NROW(found) > 0) {
-        # when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so:
-        # mo_ref("Chlamydia psittaci) = "Page, 1968" (with warning)
-        # mo_ref("Chlamydophila psittaci) = "Everett et al., 1999"
-        if (property == "ref") {
-          x[i] <- found[1, ref]
-        } else {
-          x[i] <- microorganismsDT[tsn == found[1, tsn_new], ..property][[1]]
-        }
-        renamed_note(name_old = found[1, name],
-                     name_new = microorganismsDT[tsn == found[1, tsn_new], fullname],
-                     ref_old = found[1, ref],
-                     ref_new = microorganismsDT[tsn == found[1, tsn_new], ref])
-        next
-      }
-
-      # check for uncertain results ----
-      if (allow_uncertain == TRUE) {
-        # (1) look again for old taxonomic names, now for G. species ----
-        found <- microorganisms.oldDT[name %like% x_withspaces[i]
-                                      | name %like% x_withspaces_start[i]
-                                      | name %like% x[i],]
-        if (NROW(found) > 0) {
-          if (property == "ref") {
-            x[i] <- found[1, ref]
-          } else {
-            x[i] <- microorganismsDT[tsn == found[1, tsn_new], ..property][[1]]
-          }
-          warning("Uncertain interpretation: '",
-                  x_backup[i], "' -> '", found[1, name], "'",
-                  call. = FALSE, immediate. = TRUE)
-          renamed_note(name_old = found[1, name],
-                       name_new = microorganismsDT[tsn == found[1, tsn_new], fullname],
-                       ref_old = found[1, ref],
-                       ref_new = microorganismsDT[tsn == found[1, tsn_new], ref])
-          next
-        }
-
-        # (2) try to strip off one element and check the remains
-        x_strip <- x_backup[i] %>% strsplit(" ") %>% unlist()
-        x_strip <- x_strip[1:length(x_strip) - 1]
-        x[i] <- suppressWarnings(suppressMessages(as.mo(x_strip)))
-        if (!is.na(x[i])) {
-          warning("Uncertain interpretation: '",
-                  x_backup[i], "' -> '", microorganismsDT[mo == x[i], fullname], "' (", x[i], ")",
-                  call. = FALSE, immediate. = TRUE)
-          next
-        }
-      }
-
-      # not found ----
-      x[i] <- NA_character_
-      failures <- c(failures, x_backup[i])
-
-    }
-  }
-
-  failures <- failures[!failures %in% c(NA, NULL, NaN)]
-  if (length(failures) > 0) {
-    warning("These ", length(failures) , " values could not be coerced to a valid MO code: ",
-            paste('"', unique(failures), '"', sep = "", collapse = ', '),
-            ".",
-            call. = FALSE)
-  }
-
-  # Becker ----
-  if (Becker == TRUE | Becker == "all") {
-    # See Source. It's this figure:
-    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/
-    MOs_staph <- microorganismsDT[genus == "Staphylococcus"]
-    setkey(MOs_staph, species)
-    CoNS <- MOs_staph[species %in% c("arlettae", "auricularis", "capitis",
-                                     "caprae", "carnosus", "cohnii", "condimenti",
-                                     "devriesei", "epidermidis", "equorum",
-                                     "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"), ..property][[1]]
-    CoPS <- MOs_staph[species %in% c("simiae", "agnetis", "chromogenes",
-                                     "delphini", "felis", "lutrae",
-                                     "hyicus", "intermedius",
-                                     "pseudintermedius", "pseudointermedius",
-                                     "schleiferi"), ..property][[1]]
-    x[x %in% CoNS] <- microorganismsDT[mo == 'B_STPHY_CNS', ..property][[1]][1L]
-    x[x %in% CoPS] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L]
-    if (Becker == "all") {
-      x[x == microorganismsDT[mo == 'B_STPHY_AUR', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STPHY_CPS', ..property][[1]][1L]
-    }
-  }
-
-  # Lancefield ----
-  if (Lancefield == TRUE | Lancefield == "all") {
-    # group A - S. pyogenes
-    x[x == microorganismsDT[mo == 'B_STRPTC_PYO', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPTC_GRA', ..property][[1]][1L]
-    # group B - S. agalactiae
-    x[x == microorganismsDT[mo == 'B_STRPTC_AGA', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPTC_GRB', ..property][[1]][1L]
-    # group C
-    S_groupC <- microorganismsDT %>% filter(genus == "Streptococcus",
-                                            species %in% c("equisimilis", "equi",
-                                                           "zooepidemicus", "dysgalactiae")) %>%
-      pull(property)
-    x[x %in% S_groupC] <- microorganismsDT[mo == 'B_STRPTC_GRC', ..property][[1]][1L]
-    if (Lancefield == "all") {
-      # all Enterococci
-      x[x %like% "^(Enterococcus|B_ENTRC)"] <- microorganismsDT[mo == 'B_STRPTC_GRD', ..property][[1]][1L]
-    }
-    # group F - S. anginosus
-    x[x == microorganismsDT[mo == 'B_STRPTC_ANG', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPTC_GRF', ..property][[1]][1L]
-    # group H - S. sanguinis
-    x[x == microorganismsDT[mo == 'B_STRPTC_SAN', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPTC_GRH', ..property][[1]][1L]
-    # group K - S. salivarius
-    x[x == microorganismsDT[mo == 'B_STRPTC_SAL', ..property][[1]][1L]] <- microorganismsDT[mo == 'B_STRPTC_GRK', ..property][[1]][1L]
-  }
-
-  # comply to x, which is also unique and without empty values
-  x_input_unique_nonempty <- unique(x_input[!is.na(x_input) & !is.null(x_input) & !identical(x_input, "")])
-
-  # left join the found results to the original input values (x_input)
-  df_found <- data.frame(input = as.character(x_input_unique_nonempty),
-                         found = as.character(x),
-                         stringsAsFactors = FALSE)
-  df_input <- data.frame(input = as.character(x_input),
-                         stringsAsFactors = FALSE)
-  x <- df_input %>%
-    left_join(df_found,
-              by = "input") %>%
-    pull(found)
-
-  if (property == "mo") {
-    class(x) <- "mo"
-  } else if (property == "tsn") {
-    x <- as.integer(x)
-  }
-
-  x
-}
-
-renamed_note <- function(name_old, name_new, ref_old = "", ref_new = "") {
-  if (!is.na(ref_old)) {
-    ref_old <- paste0(" (", ref_old, ")")
-  } else {
-    ref_old <- ""
-  }
-  if (!is.na(ref_new)) {
-    ref_new <- paste0(" (", ref_new, ")")
-  } else {
-    ref_new <- ""
-  }
-  base::message(paste0("Note: '", name_old, "'", ref_old, " was renamed '", name_new, "'", ref_new))
-}
-
-#' @exportMethod print.mo
-#' @export
-#' @noRd
-print.mo <- function(x, ...) {
-  cat("Class 'mo'\n")
-  x_names <- names(x)
-  x <- as.character(x)
-  names(x) <- x_names
-  print.default(x, quote = FALSE)
-}
-
-#' @exportMethod as.data.frame.mo
-#' @export
-#' @noRd
-as.data.frame.mo <- function (x, ...) {
-  # same as as.data.frame.character but with removed stringsAsFactors, since it will be class "mo"
-  nm <- paste(deparse(substitute(x), width.cutoff = 500L),
-              collapse = " ")
-  if (!"nm" %in% names(list(...))) {
-    as.data.frame.vector(x, ..., nm = nm)
-  } else {
-    as.data.frame.vector(x, ...)
-  }
-}
-
-#' @exportMethod pull.mo
-#' @export
-#' @importFrom dplyr pull
-#' @noRd
-pull.mo <- function(.data, ...) {
-  pull(as.data.frame(.data), ...)
-}
@@ -1,471 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Property of a microorganism
-#'
-#' Use these functions to return a specific property of a microorganism from the \code{\link{microorganisms}} data set. All input values will be evaluated internally with \code{\link{as.mo}}.
-#' @param x any (vector of) text that can be coerced to a valid microorganism code with \code{\link{as.mo}}
-#' @param property one of the column names of one of the \code{\link{microorganisms}} data set or \code{"shortname"}
-#' @param language language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.
-#' @param ... other parameters passed on to \code{\link{as.mo}}
-#' @details All functions will return the most recently known taxonomic property according to ITIS, except for \code{mo_ref}, \code{mo_authors} and \code{mo_year}. This leads to the following results:
-#' \itemize{
-#'   \item{\code{mo_fullname("Chlamydia psittaci")} will return \code{"Chlamydophila psittaci"} (with a warning about the renaming)}
-#'   \item{\code{mo_ref("Chlamydia psittaci")} will return \code{"Page, 1968"} (with a warning about the renaming)}
-#'   \item{\code{mo_ref("Chlamydophila psittaci")} will return \code{"Everett et al., 1999"} (without a warning)}
-#' }
-#' @inheritSection get_locale Supported languages
-#' @inheritSection as.mo ITIS
-#' @inheritSection as.mo Source
-#' @rdname mo_property
-#' @name mo_property
-#' @return \itemize{
-#'   \item{An \code{integer} in case of \code{mo_TSN} and \code{mo_year}}
-#'   \item{A \code{list} in case of \code{mo_taxonomy}}
-#'   \item{A \code{character} in all other cases}
-#' }
-#' @export
-#' @seealso \code{\link{microorganisms}}
-#' @examples
-#' # All properties of Escherichia coli
-#' ## taxonomic properties
-#' mo_kingdom("E. coli")         # "Bacteria"
-#' mo_subkingdom("E. coli")      # "Negibacteria"
-#' mo_phylum("E. coli")          # "Proteobacteria"
-#' mo_class("E. coli")           # "Gammaproteobacteria"
-#' mo_order("E. coli")           # "Enterobacteriales"
-#' mo_family("E. coli")          # "Enterobacteriaceae"
-#' mo_genus("E. coli")           # "Escherichia"
-#' mo_species("E. coli")         # "coli"
-#' mo_subspecies("E. coli")      # NA
-#' mo_TSN("E. coli")             # 285 (Taxonomic Serial Number)
-#'
-#' ## colloquial properties
-#' mo_fullname("E. coli")        # "Escherichia coli"
-#' mo_shortname("E. coli")       # "E. coli"
-#'
-#' ## other properties
-#' mo_gramstain("E. coli")       # "Gram negative"
-#' mo_type("E. coli")            # "Bacteria" (equal to kingdom)
-#'
-#' ## scientific reference
-#' mo_ref("E. coli")             # "Castellani and Chalmers, 1919"
-#' mo_authors("E. coli")         # "Castellani and Chalmers"
-#' mo_year("E. coli")            # 1919
-#'
-#'
-#' # Abbreviations known in the field
-#' mo_genus("MRSA")              # "Staphylococcus"
-#' mo_species("MRSA")            # "aureus"
-#' mo_shortname("MRSA")          # "S. aureus"
-#' mo_gramstain("MRSA")          # "Gram positive"
-#'
-#' mo_genus("VISA")              # "Staphylococcus"
-#' mo_species("VISA")            # "aureus"
-#'
-#'
-#' # Known subspecies
-#' mo_genus("doylei")            # "Campylobacter"
-#' mo_species("doylei")          # "jejuni"
-#' mo_fullname("doylei")         # "Campylobacter jejuni doylei"
-#'
-#' mo_fullname("K. pneu rh")     # "Klebsiella pneumoniae rhinoscleromatis"
-#' mo_shortname("K. pneu rh")    # "K. pneumoniae"
-#'
-#'
-#' # Becker classification, see ?as.mo
-#' mo_fullname("S. epi")                     # "Staphylococcus epidermidis"
-#' mo_fullname("S. epi", Becker = TRUE)      # "Coagulase Negative Staphylococcus (CoNS)"
-#' mo_shortname("S. epi")                    # "S. epidermidis"
-#' mo_shortname("S. epi", Becker = TRUE)     # "CoNS"
-#'
-#' # Lancefield classification, see ?as.mo
-#' mo_fullname("S. pyo")                     # "Streptococcus pyogenes"
-#' mo_fullname("S. pyo", Lancefield = TRUE)  # "Streptococcus group A"
-#' mo_shortname("S. pyo")                    # "S. pyogenes"
-#' mo_shortname("S. pyo", Lancefield = TRUE) # "GAS" ('Group A streptococci')
-#'
-#'
-#' # Language support for German, Dutch, Spanish, Portuguese, Italian and French
-#' mo_gramstain("E. coli", language = "de")  # "Gramnegativ"
-#' mo_gramstain("E. coli", language = "nl")  # "Gram-negatief"
-#' mo_gramstain("E. coli", language = "es")  # "Gram negativo"
-#'
-#' # mo_type is equal to mo_kingdom, but mo_kingdom will remain official
-#' mo_kingdom("E. coli")                     # "Bacteria" on a German system
-#' mo_type("E. coli")                        # "Bakterien" on a German system
-#' mo_type("E. coli")                        # "Bacteria" on an English system
-#'
-#' mo_fullname("S. pyogenes",
-#'             Lancefield = TRUE,
-#'             language = "de")              # "Streptococcus Gruppe A"
-#' mo_fullname("S. pyogenes",
-#'             Lancefield = TRUE,
-#'             language = "nl")              # "Streptococcus groep A"
-#'
-#'
-#' # Get a list with the complete taxonomy (subkingdom to subspecies)
-#' mo_taxonomy("E. coli")
-mo_fullname <- function(x, language = get_locale(), ...) {
-  x <- mo_validate(x = x, property = "fullname", ...)
-  mo_translate(x, language = language)
-}
-
-#' @rdname mo_property
-#' @importFrom dplyr %>% left_join mutate pull
-#' @export
-mo_shortname <- function(x, language = get_locale(), ...) {
-  dots <- list(...)
-  Becker <- dots$Becker
-  if (is.null(Becker)) {
-    Becker <- FALSE
-  }
-  Lancefield <- dots$Lancefield
-  if (is.null(Lancefield)) {
-    Lancefield <- FALSE
-  }
-  if (Becker %in% c(TRUE, "all") | Lancefield == TRUE) {
-    res1 <- AMR::as.mo(x, Becker = FALSE, Lancefield = FALSE, reference_df = dots$reference_df)
-    res2 <- suppressWarnings(AMR::as.mo(res1, ...))
-    res2_fullname <- mo_fullname(res2, language = language)
-    res2_fullname[res2_fullname %like% " \\(CoNS\\)"] <- "CoNS"
-    res2_fullname[res2_fullname %like% " \\(CoPS\\)"] <- "CoPS"
-    res2_fullname[res2_fullname %like% " \\(KNS\\)"] <- "KNS"
-    res2_fullname[res2_fullname %like% " \\(KPS\\)"] <- "KPS"
-    res2_fullname[res2_fullname %like% " \\(CNS\\)"] <- "CNS"
-    res2_fullname[res2_fullname %like% " \\(CPS\\)"] <- "CPS"
-    res2_fullname <- gsub("Streptococcus (group|Gruppe|gruppe|groep|grupo|gruppo|groupe) (.)",
-                          "G\\2S",
-                          res2_fullname) # turn "Streptococcus group A" and "Streptococcus grupo A" to "GAS"
-    res2_fullname_vector <- res2_fullname[res2_fullname == mo_fullname(res1)]
-    res2_fullname[res2_fullname == mo_fullname(res1)] <- paste0(substr(mo_genus(res2_fullname_vector), 1, 1),
-                                                                ". ",
-                                                                suppressWarnings(mo_species(res2_fullname_vector)))
-    if (sum(res1 == res2, na.rm = TRUE) > 0) {
-      res1[res1 == res2] <- paste0(substr(mo_genus(res1[res1 == res2]), 1, 1),
-                                   ". ",
-                                   suppressWarnings(mo_species(res1[res1 == res2])))
-    }
-    res1[res1 != res2] <- res2_fullname
-    result <- as.character(res1)
-  } else {
-    x <- AMR::as.mo(x, ...)
-    suppressWarnings(
-      result <- data.frame(mo = x) %>%
-        left_join(AMR::microorganisms, by = "mo") %>%
-        mutate(shortname = ifelse(!is.na(genus) & !is.na(species), paste0(substr(genus, 1, 1), ". ", species), NA_character_)) %>%
-        pull(shortname)
-    )
-  }
-  mo_translate(result, language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_subspecies <- function(x, language = get_locale(), ...) {
-  mo_translate(mo_validate(x = x, property = "subspecies", ...), language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_species <- function(x, language = get_locale(), ...) {
-  mo_translate(mo_validate(x = x, property = "species", ...), language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_genus <- function(x, language = get_locale(), ...) {
-  mo_translate(mo_validate(x = x, property = "genus", ...), language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_family <- function(x, ...) {
-  mo_validate(x = x, property = "family", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_order <- function(x, ...) {
-  mo_validate(x = x, property = "order", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_class <- function(x, ...) {
-  mo_validate(x = x, property = "class", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_phylum <- function(x, ...) {
-  mo_validate(x = x, property = "phylum", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_subkingdom <- function(x, ...) {
-  mo_validate(x = x, property = "subkingdom", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_kingdom <- function(x, ...) {
-  mo_validate(x = x, property = "kingdom", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_type <- function(x, language = get_locale(), ...) {
-  mo_translate(mo_validate(x = x, property = "kingdom", ...), language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_gramstain <- function(x, language = get_locale(), ...) {
-  mo_translate(mo_validate(x = x, property = "gramstain", ...), language = language)
-}
-
-#' @rdname mo_property
-#' @export
-mo_TSN  <- function(x, ...) {
-  mo_validate(x = x, property = "tsn", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_ref <- function(x, ...) {
-  mo_validate(x = x, property = "ref", ...)
-}
-
-#' @rdname mo_property
-#' @export
-mo_authors <- function(x, ...) {
-  x <- mo_validate(x = x, property = "ref", ...)
-  # 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)])
-  x
-}
-
-#' @rdname mo_property
-#' @export
-mo_year <- function(x, ...) {
-  x <- mo_validate(x = x, property = "ref", ...)
-  # get last 4 digits
-  x[!is.na(x)] <- gsub(".*([0-9]{4})$", "\\1", x[!is.na(x)])
-  as.integer(x)
-}
-
-#' @rdname mo_property
-#' @export
-mo_taxonomy <- function(x, ...) {
-  x <- AMR::as.mo(x, ...)
-  base::list(subkingdom = mo_subkingdom(x),
-             phylum = mo_phylum(x),
-             class = mo_class(x),
-             order = mo_order(x),
-             family = mo_family(x),
-             genus = mo_genus(x),
-             species = mo_species(x),
-             subspecies = mo_subspecies(x))
-}
-
-#' @rdname mo_property
-#' @importFrom data.table data.table as.data.table setkey
-#' @export
-mo_property <- function(x, property = 'fullname', language = get_locale(), ...) {
-  if (length(property) != 1L) {
-    stop("'property' must be of length 1.")
-  }
-  if (!property %in% colnames(AMR::microorganisms)) {
-    stop("invalid property: '", property, "' - use a column name of the `microorganisms` data set")
-  }
-
-  mo_translate(mo_validate(x = x, property = property, ...), language = language)
-}
-
-#' @importFrom dplyr %>% case_when
-mo_translate <- function(x, language) {
-  if (is.null(language)) {
-    return(x)
-  }
-  if (language %in% c("en", "")) {
-    return(x)
-  }
-
-  supported <- c("en", "de", "nl", "es", "pt", "it", "fr")
-  if (!language %in% supported) {
-    stop("Unsupported language: '", language, "' - use one of: ", paste0("'", sort(supported), "'", collapse = ", "), call. = FALSE)
-  }
-
-  x_tobetranslated <- grepl(x = x,
-                            pattern = "(Coagulase Negative Staphylococcus|Coagulase Positive Staphylococcus|Beta-haemolytic Streptococcus|unknown Gram negatives|unknown Gram positives|CoNS|CoPS|no MO|Gram negative|Gram positive|Bacteria|Fungi|Protozoa|biogroup|biotype|vegetative|group|Group)")
-
-  if (sum(x_tobetranslated, na.rm = TRUE) == 0) {
-    return(x)
-  }
-
-  # only translate the ones that need translation
-  x[x_tobetranslated] <- case_when(
-    # German
-    language == "de" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Koagulase-negative Staphylococcus", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Koagulase-positive Staphylococcus", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Beta-h\u00e4molytischer Streptococcus", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "unbekannte Gramnegativen", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "unbekannte Grampositiven", ., fixed = TRUE) %>%
-      gsub("(CoNS)",           "(KNS)", ., fixed = TRUE) %>%
-      gsub("(CoPS)",           "(KPS)", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(kein MO)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gramnegativ", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Grampositiv", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Bakterien", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Hefen/Pilze", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "Protozoen", ., fixed = TRUE) %>%
-      gsub("biogroup",         "Biogruppe", ., fixed = TRUE) %>%
-      gsub("biotype",          "Biotyp", ., fixed = TRUE) %>%
-      gsub("vegetative",       "vegetativ", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1Gruppe", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Gruppe", .) %>%
-      iconv(to = "UTF-8"),
-
-    # Dutch
-    language == "nl" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Coagulase-negatieve Staphylococcus", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Coagulase-positieve Staphylococcus", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Beta-hemolytische Streptococcus", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "onbekende Gram-negatieven", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "onbekende Gram-positieven", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(geen MO)", ., fixed = TRUE) %>%
-      gsub("(CoNS)",           "(CNS)", ., fixed = TRUE) %>%
-      gsub("(CoPS)",           "(CPS)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gram-negatief", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Gram-positief", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Bacteri\u00ebn", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Schimmels/gisten", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "protozo\u00ebn", ., fixed = TRUE) %>%
-      gsub("biogroup",         "biogroep", ., fixed = TRUE) %>%
-      # gsub("biotype",          "biotype", ., fixed = TRUE) %>%
-      gsub("vegetative",       "vegetatief", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1groep", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Groep", .) %>%
-      iconv(to = "UTF-8"),
-
-    # Spanish
-    language == "es" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Staphylococcus coagulasa negativo", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Staphylococcus coagulasa positivo", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Streptococcus Beta-hemol\u00edtico", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "Gram negativos desconocidos", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "Gram positivos desconocidos", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(sin MO)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gram negativo", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Gram positivo", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Bacterias", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Hongos", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "Protozoarios", ., fixed = TRUE) %>%
-      gsub("biogroup",         "biogrupo", ., fixed = TRUE) %>%
-      gsub("biotype",          "biotipo", ., fixed = TRUE) %>%
-      gsub("vegetative",       "vegetativo", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1grupo", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Grupo", .) %>%
-      iconv(to = "UTF-8"),
-
-    # Italian
-    language == "it" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Staphylococcus negativo coagulasi", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Staphylococcus positivo coagulasi", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Streptococcus Beta-emolitico", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "Gram negativi sconosciuti", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "Gram positivi sconosciuti", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(non MO)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gram negativo", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Gram positivo", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Batteri", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Fungo", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "Protozoi", ., fixed = TRUE) %>%
-      gsub("biogroup",         "biogruppo", ., fixed = TRUE) %>%
-      gsub("biotype",          "biotipo", ., fixed = TRUE) %>%
-      gsub("vegetative",       "vegetativo", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1gruppo", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Gruppo", .),
-
-    # French
-    language == "fr" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Staphylococcus \u00e0 coagulase n\u00e9gative", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Staphylococcus \u00e0 coagulase positif", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Streptococcus B\u00eata-h\u00e9molytique", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "Gram n\u00e9gatifs inconnus", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "Gram positifs inconnus", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(pas MO)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gram n\u00e9gatif", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Gram positif", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Bact\u00e9ries", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Champignons", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "Protozoaires", ., fixed = TRUE) %>%
-      gsub("biogroup",         "biogroupe", ., fixed = TRUE) %>%
-      # gsub("biotype",          "biotype", ., fixed = TRUE) %>%
-      gsub("vegetative",       "v\u00e9g\u00e9tatif", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1groupe", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Groupe", .) %>%
-      iconv(to = "UTF-8"),
-
-    # Portuguese
-    language == "pt" ~ x[x_tobetranslated] %>%
-      gsub("Coagulase Negative Staphylococcus","Staphylococcus coagulase negativo", ., fixed = TRUE) %>%
-      gsub("Coagulase Positive Staphylococcus","Staphylococcus coagulase positivo", ., fixed = TRUE) %>%
-      gsub("Beta-haemolytic Streptococcus",    "Streptococcus Beta-hemol\u00edtico", ., fixed = TRUE) %>%
-      gsub("unknown Gram negatives",           "Gram negativos desconhecidos", ., fixed = TRUE) %>%
-      gsub("unknown Gram positives",           "Gram positivos desconhecidos", ., fixed = TRUE) %>%
-      gsub("(no MO)",          "(sem MO)", ., fixed = TRUE) %>%
-      gsub("Gram negative",    "Gram negativo", ., fixed = TRUE) %>%
-      gsub("Gram positive",    "Gram positivo", ., fixed = TRUE) %>%
-      gsub("Bacteria",         "Bact\u00e9rias", ., fixed = TRUE) %>%
-      gsub("Fungi",            "Fungos", ., fixed = TRUE) %>%
-      gsub("Protozoa",         "Protozo\u00e1rios", ., fixed = TRUE) %>%
-      gsub("biogroup",         "biogrupo", ., fixed = TRUE) %>%
-      gsub("biotype",          "bi\u00f3tipo", ., fixed = TRUE) %>%
-      gsub("vegetative",       "vegetativo", ., fixed = TRUE) %>%
-      gsub("([([ ]*?)group",   "\\1grupo", .) %>%
-      gsub("([([ ]*?)Group",   "\\1Grupo", .) %>%
-      iconv(to = "UTF-8"))
-
-  x
-
-}
-
-mo_validate <- function(x, property, ...) {
-
-  dots <- list(...)
-  Becker <- dots$Becker
-  if (is.null(Becker)) {
-    Becker <- FALSE
-  }
-  Lancefield <- dots$Lancefield
-  if (is.null(Lancefield)) {
-    Lancefield <- FALSE
-  }
-
-  if (!all(x %in% microorganismsDT[[property]])
-      | Becker %in% c(TRUE, "all")
-      | Lancefield %in% c(TRUE, "all")) {
-    exec_as.mo(x, property = property, ...)
-  } else {
-    x
-  }
-}
@@ -1,56 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Symbol of a p value
-#'
-#' Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
-#' @param p p value
-#' @param emptychar text to show when \code{p > 0.1}
-#' @return Text
-#' @export
-p.symbol <- function(p, emptychar = " ") {
-  setting.bak <- options()$scipen
-  options(scipen = 999)
-  s <- vector(mode = "character", length = length(p))
-  for (i in 1:length(p)) {
-    if (is.na(p[i])) {
-      s[i] <- NA_character_
-      next
-    }
-    if (p[i] > 1) {
-      s[i] <- NA_character_
-      next
-    } else {
-      p_test <- p[i]
-    }
-
-    if (p_test > 0.1) {
-      s[i] <- emptychar
-    } else if (p_test > 0.05) {
-      s[i] <- '.'
-    } else if (p_test > 0.01) {
-      s[i] <- '*'
-    } else if (p_test > 0.001) {
-      s[i] <- '**'
-    } else if (p_test >= 0) {
-      s[i] <- '***'
-    }
-  }
-  options(scipen = setting.bak)
-  s
-}
@@ -1,329 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Calculate resistance of isolates
-#'
-#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
-#'
-#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
-#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
-#' @param minimum the minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
-#' @param as_percent a logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.
-#' @param also_single_tested a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of \code{portion_S} and R in case of \code{portion_R}). \strong{This would lead to selection bias in almost all cases.}
-#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
-#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
-#' @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. IR (susceptible vs. non-susceptible)
-#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
-#'
-#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
-#'
-#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
-#'
-#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
-#' \if{html}{
-#'   \cr\cr
-#'   To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
-#'   \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
-#'   To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
-#'   \cr
-#'   For two antibiotics:
-#'   \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
-#'   \cr
-#'   For three antibiotics:
-#'   \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
-#'   \cr
-#'   And so on.
-#' }
-#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
-#'
-#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
-#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.
-#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
-#' @return Double or, when \code{as_percent = TRUE}, a character.
-#' @rdname portion
-#' @name portion
-#' @export
-#' @examples
-#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
-#' ?septic_patients
-#'
-#' # Calculate resistance
-#' portion_R(septic_patients$amox)
-#' portion_IR(septic_patients$amox)
-#'
-#' # Or susceptibility
-#' portion_S(septic_patients$amox)
-#' portion_SI(septic_patients$amox)
-#'
-
-#' # Do the above with pipes:
-#' library(dplyr)
-#' septic_patients %>% portion_R(amox)
-#' septic_patients %>% portion_IR(amox)
-#' septic_patients %>% portion_S(amox)
-#' septic_patients %>% portion_SI(amox)
-#'
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(p = portion_S(cipr),
-#'             n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
-#'
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(R = portion_R(cipr, as_percent = TRUE),
-#'             I = portion_I(cipr, as_percent = TRUE),
-#'             S = portion_S(cipr, as_percent = TRUE),
-#'             n = n_rsi(cipr), # works like n_distinct in dplyr
-#'             total = n())     # NOT the amount of tested isolates!
-#'
-#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
-#' # so we can see that combination therapy does a lot more than mono therapy:
-#' septic_patients %>% portion_S(amcl)       # S = 67.1%
-#' septic_patients %>% count_all(amcl)       # n = 1576
-#'
-#' septic_patients %>% portion_S(gent)       # S = 74.0%
-#' septic_patients %>% count_all(gent)       # n = 1855
-#'
-#' septic_patients %>% portion_S(amcl, gent) # S = 92.0%
-#' septic_patients %>% count_all(amcl, gent) # n = 1517
-#'
-#'
-#' septic_patients %>%
-#'   group_by(hospital_id) %>%
-#'   summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
-#'             cipro_n = count_all(cipr),
-#'             genta_p = portion_S(gent, as_percent = TRUE),
-#'             genta_n = count_all(gent),
-#'             combination_p = portion_S(cipr, gent, as_percent = TRUE),
-#'             combination_n = count_all(cipr, gent))
-#'
-#' # Get portions S/I/R immediately of all rsi columns
-#' septic_patients %>%
-#'   select(amox, cipr) %>%
-#'   portion_df(translate = FALSE)
-#'
-#' # It also supports grouping variables
-#' septic_patients %>%
-#'   select(hospital_id, amox, cipr) %>%
-#'   group_by(hospital_id) %>%
-#'   portion_df(translate = FALSE)
-#'
-#'
-#' \dontrun{
-#'
-#' # calculate current empiric combination therapy of Helicobacter gastritis:
-#' my_table %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Helicobacter") %>%
-#'   summarise(p = portion_S(amox, metr),  # amoxicillin with metronidazole
-#'             n = count_all(amox, metr))
-#' }
-portion_R <- function(...,
-                      minimum = 30,
-                      as_percent = FALSE,
-                      also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "R",
-           include_I = FALSE,
-           minimum = minimum,
-           as_percent = as_percent,
-           also_single_tested = also_single_tested,
-           only_count = FALSE)
-}
-
-#' @rdname portion
-#' @export
-portion_IR <- function(...,
-                       minimum = 30,
-                       as_percent = FALSE,
-                       also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "R",
-           include_I = TRUE,
-           minimum = minimum,
-           as_percent = as_percent,
-           also_single_tested = also_single_tested,
-           only_count = FALSE)
-}
-
-#' @rdname portion
-#' @export
-portion_I <- function(...,
-                      minimum = 30,
-                      as_percent = FALSE,
-                      also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "I",
-           include_I = FALSE,
-           minimum = minimum,
-           as_percent = as_percent,
-           also_single_tested = also_single_tested,
-           only_count = FALSE)
-}
-
-#' @rdname portion
-#' @export
-portion_SI <- function(...,
-                       minimum = 30,
-                       as_percent = FALSE,
-                       also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "S",
-           include_I = TRUE,
-           minimum = minimum,
-           as_percent = as_percent,
-           also_single_tested = also_single_tested,
-           only_count = FALSE)
-}
-
-#' @rdname portion
-#' @export
-portion_S <- function(...,
-                      minimum = 30,
-                      as_percent = FALSE,
-                      also_single_tested = FALSE) {
-  rsi_calc(...,
-           type = "S",
-           include_I = FALSE,
-           minimum = minimum,
-           as_percent = as_percent,
-           also_single_tested = also_single_tested,
-           only_count = FALSE)
-}
-
-#' @rdname portion
-#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
-#' @export
-portion_df <- function(data,
-                       translate_ab = getOption("get_antibiotic_names", "official"),
-                       minimum = 30,
-                       as_percent = FALSE,
-                       combine_IR = FALSE) {
-
-  if (!"data.frame" %in% class(data)) {
-    stop("`portion_df` must be called on a data.frame")
-  }
-
-  if (data %>% select_if(is.rsi) %>% ncol() == 0) {
-    stop("No columns with class 'rsi' found. See ?as.rsi.")
-  }
-
-  if (as.character(translate_ab) == "TRUE") {
-    translate_ab <- "official"
-  }
-  options(get_antibiotic_names = translate_ab)
-
-  resS <- summarise_if(.tbl = data,
-                       .predicate = is.rsi,
-                       .funs = portion_S,
-                       minimum = minimum,
-                       as_percent = as_percent) %>%
-    mutate(Interpretation = "S") %>%
-    select(Interpretation, everything())
-
-  if (combine_IR == FALSE) {
-    resI <- summarise_if(.tbl = data,
-                         .predicate = is.rsi,
-                         .funs = portion_I,
-                         minimum = minimum,
-                         as_percent = as_percent) %>%
-      mutate(Interpretation = "I") %>%
-      select(Interpretation, everything())
-
-    resR <- summarise_if(.tbl = data,
-                         .predicate = is.rsi,
-                         .funs = portion_R,
-                         minimum = minimum,
-                         as_percent = as_percent) %>%
-      mutate(Interpretation = "R") %>%
-      select(Interpretation, everything())
-
-    data.groups <- group_vars(data)
-
-    res <- bind_rows(resS, resI, resR) %>%
-      mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
-      tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
-  } else {
-    resIR <- summarise_if(.tbl = data,
-                          .predicate = is.rsi,
-                          .funs = portion_IR,
-                          minimum = minimum,
-                          as_percent = as_percent) %>%
-      mutate(Interpretation = "IR") %>%
-      select(Interpretation, everything())
-
-    data.groups <- group_vars(data)
-
-    res <- bind_rows(resS, resIR) %>%
-      mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
-      tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
-  }
-
-  if (!translate_ab == FALSE) {
-    if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
-      stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
-    }
-    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
-  }
-
-  res
-}
-
-
-#' Calculate resistance of isolates
-#'
-#' This function is deprecated. Use the \code{\link{portion}} functions instead.
-#' @inheritParams portion
-#' @param ab1,ab2  vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
-#' @param interpretation antimicrobial interpretation to check for
-#' @param ... deprecated parameters to support usage on older versions
-#' @importFrom dplyr tibble case_when
-#' @export
-rsi <- function(ab1,
-                ab2 = NULL,
-                interpretation = "IR",
-                minimum = 30,
-                as_percent = FALSE,
-                ...) {
-
-  .Deprecated(new = paste0("portion_", interpretation))
-
-  if (all(is.null(ab2))) {
-    df <- tibble(ab1 = ab1)
-  } else {
-    df <- tibble(ab1 = ab1,
-                 ab2 = ab2)
-  }
-
-  if (!interpretation %in% c("S", "SI", "IS", "I", "RI", "IR", "R")) {
-    stop("invalid interpretation")
-  }
-
-  result <- case_when(
-    interpretation == "S"             ~ portion_S(df, minimum = minimum, as_percent = FALSE),
-    interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
-    interpretation == "I"             ~ portion_I(df, minimum = minimum, as_percent = FALSE),
-    interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
-    interpretation == "R"             ~ portion_R(df, minimum = minimum, as_percent = FALSE))
-
-  if (as_percent == TRUE) {
-    percent(result, force_zero = TRUE)
-  } else {
-    result
-  }
-}
@@ -1,183 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Read data from 4D database
-#'
-#' This function is only useful for the MMB department of the UMCG. Use this function to \strong{import data by just defining the \code{file} parameter}. It will automatically transform birth dates and calculate patients age, translate the column names to English, transform the \code{mo} with \code{\link{as.mo}} and transform all antimicrobial columns with \code{\link{as.rsi}}.
-#' @inheritParams utils::read.table
-#' @param info a logical to indicate whether info about the import should be printed, defaults to \code{TRUE} in interactive sessions
-#' @details Column names will be transformed, but the original column names are set as a "label" attribute and can be seen in e.g. RStudio Viewer.
-#' @export
-read.4D <- function(file,
-                    info = interactive(),
-                    header = TRUE,
-                    row.names = NULL,
-                    sep = "\t",
-                    quote = "\"'",
-                    dec = ",",
-                    na.strings = c("NA", "", "."),
-                    skip = 2,
-                    check.names = TRUE,
-                    strip.white = TRUE,
-                    fill = TRUE,
-                    blank.lines.skip = TRUE,
-                    stringsAsFactors = FALSE,
-                    fileEncoding = "UTF-8",
-                    encoding = "UTF-8") {
-
-  if (info == TRUE) {
-    message("Importing ", file, "... ", appendLF = FALSE)
-  }
-  data_4D <- utils::read.table(file = file,
-                               row.names = row.names,
-                               header = header,
-                               sep = sep,
-                               quote = quote,
-                               dec = dec,
-                               na.strings = na.strings,
-                               skip = skip,
-                               check.names = check.names,
-                               strip.white = strip.white,
-                               fill = fill,
-                               blank.lines.skip = blank.lines.skip,
-                               stringsAsFactors = stringsAsFactors,
-                               fileEncoding = fileEncoding,
-                               encoding = encoding)
-
-  # helper functions
-  to_date_4D <- function(x) {
-    date_regular <- as.Date(x, format = "%d-%m-%y")
-    posixlt <- as.POSIXlt(date_regular)
-    # born after today will be born 100 years ago
-    # based on https://stackoverflow.com/a/3312971/4575331
-    posixlt[date_regular > Sys.Date() & !is.na(posixlt)]$year <- posixlt[date_regular > Sys.Date() & !is.na(posixlt)]$year - 100
-    as.Date(posixlt)
-  }
-  to_age_4D <- function(from, to) {
-    from_lt = as.POSIXlt(from)
-    to_lt = as.POSIXlt(to)
-
-    age = to_lt$year - from_lt$year
-
-    ifelse(to_lt$mon < from_lt$mon |
-             (to_lt$mon == from_lt$mon & to_lt$mday < from_lt$mday),
-           age - 1, age)
-  }
-
-  if (info == TRUE) {
-    message("OK\nTransforming column names... ", appendLF = FALSE)
-  }
-  if ("row.names" %in% colnames(data_4D) & all(is.na(data_4D[, ncol(data_4D)]))) {
-    # remove first column name "row.names" and remove last empty column
-    colnames(data_4D) <- c(colnames(data_4D)[2:ncol(data_4D)], "_skip_last")
-    data_4D <- data_4D[, -ncol(data_4D)]
-  }
-
-  colnames(data_4D) <- tolower(colnames(data_4D))
-  if (all(c("afnamedat", "gebdatum") %in% colnames(data_4D))) {
-    # add age
-    data_4D$age <- NA_integer_
-  }
-  cols_wanted <- c("patientnr", "gebdatum", "age", "mv", "monsternr", "afnamedat", "bepaling",
-                   "afd.", "spec", "mat", "matbijz.",  "mocode",
-                   "amfo", "amox", "anid", "azit", "casp", "cecl", "cefe", "cfcl",
-                   "cfot", "cfox", "cfta", "cftr", "cfur", "chlo", "cipr", "clin",
-                   "cocl", "ctta", "dapt", "doxy", "eryt", "fluo", "fluz", "fosf",
-                   "fusi", "gehi", "gent", "imip", "kana", "levo", "line", "mero",
-                   "metr", "mico", "mino", "moxi", "mupi", "nali", "nitr", "norf",
-                   "oxac", "peni", "pipe", "pita", "poly", "posa", "quda", "rifa",
-                   "spat", "teic", "tige", "tobr", "trim", "trsu", "vana", "vanb",
-                   "vanc", "vori")
-  # this ones actually exist
-  cols_wanted <- cols_wanted[cols_wanted %in% colnames(data_4D)]
-  # order of columns
-  data_4D <- data_4D[, cols_wanted]
-
-  # backup original column names
-  colnames.bak <- toupper(colnames(data_4D))
-  colnames.bak[colnames.bak == "AGE"] <- NA_character_
-
-  # rename of columns
-  colnames(data_4D) <- gsub("patientnr", "patient_id", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("gebdatum", "date_birth", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("mv", "gender", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("monsternr", "sample_id", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("afnamedat", "date_received", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("bepaling", "sample_test", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("afd.", "department", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("spec", "specialty", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("matbijz.", "specimen_type", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("mat", "specimen_group", colnames(data_4D), fixed = TRUE)
-  colnames(data_4D) <- gsub("mocode", "mo", colnames(data_4D), fixed = TRUE)
-
-  if (info == TRUE) {
-    message("OK\nTransforming dates and age... ", appendLF = FALSE)
-  }
-  if ("date_birth" %in% colnames(data_4D)) {
-    data_4D$date_birth <- to_date_4D(data_4D$date_birth)
-
-  }
-  if ("date_received" %in% colnames(data_4D)) {
-    data_4D$date_received <- to_date_4D(data_4D$date_received)
-  }
-  if ("age" %in% colnames(data_4D)) {
-    data_4D$age <- to_age_4D(data_4D$date_birth, data_4D$date_received)
-  }
-  if ("gender" %in% colnames(data_4D)) {
-    data_4D$gender[data_4D$gender == "V"] <- "F"
-  }
-
-  if (info == TRUE) {
-    message("OK\nTransforming MO codes... ", appendLF = FALSE)
-  }
-  if ("mo" %in% colnames(data_4D)) {
-    data_4D$mo <- as.mo(data_4D$mo)
-    # column right of mo is:
-    drug1 <- colnames(data_4D)[grep("^mo$", colnames(data_4D)) + 1]
-    if (!is.na(drug1)) {
-      # and last is:
-      drug_last <- colnames(data_4D)[length(data_4D)]
-      # transform those to rsi:
-      data_4D <- suppressWarnings(mutate_at(data_4D, vars(drug1:drug_last), as.rsi))
-    }
-  }
-
-  # set original column names as label (can be seen in RStudio Viewer)
-  if (info == TRUE) {
-    message("OK\nSetting original column names as label... ", appendLF = FALSE)
-  }
-  for (i in 1:ncol(data_4D)) {
-    if (!is.na(colnames.bak[i])) {
-      attr(data_4D[, i], "label") <- colnames.bak[i]
-    }
-  }
-
-  if (info == TRUE) {
-    message("OK\nSetting query as label to data.frame... ", appendLF = FALSE)
-  }
-  qry <- readLines(con <- file(file, open="r"))[1]
-  close(con)
-  attr(data_4D, "label") <- qry
-
-  if (info == TRUE) {
-    message("OK")
-  }
-
-  data_4D
-}
-
@@ -1,287 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Predict antimicrobial resistance
-#'
-#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
-#' @inheritParams first_isolate
-#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
-#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already
-#' @param year_min lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}
-#' @param year_max highest year to use in the prediction model, defaults to 15 years after today
-#' @param year_every unit of sequence between lowest year found in the data and \code{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. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
-#' @param I_as_R treat \code{I} as \code{R}
-#' @param preserve_measurements logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be \code{NA}.
-#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
-#' @return \code{data.frame} with columns:
-#' \itemize{
-#'   \item{\code{year}}
-#'   \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
-#'   \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
-#'   \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
-#'   \item{\code{observations}, the total number of observations, i.e. S + I + R}
-#'   \item{\code{observed}, the original observed values}
-#'   \item{\code{estimated}, the estimated values, calculated by the model}
-#' }
-#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
-#' @rdname resistance_predict
-#' @export
-#' @importFrom stats predict glm lm
-#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange case_when
-# @importFrom tidyr spread
-#' @examples
-#' \dontrun{
-#' # use it with base R:
-#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
-#'                    col_ab = "amcl", col_date = "date")
-#'
-#' # or use dplyr so you can actually read it:
-#' library(dplyr)
-#' tbl %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Haemophilus") %>%
-#'   resistance_predict(amcl, date)
-#' }
-#'
-#'
-#' # real live example:
-#' library(dplyr)
-#' septic_patients %>%
-#'   # get bacteria properties like genus and species
-#'   left_join_microorganisms("mo") %>%
-#'   # calculate first isolates
-#'   mutate(first_isolate = first_isolate(.)) %>%
-#'   # filter on first E. coli isolates
-#'   filter(genus == "Escherichia",
-#'          species == "coli",
-#'          first_isolate == TRUE) %>%
-#'   # predict resistance of cefotaxime for next years
-#'   resistance_predict(col_ab = "cfot",
-#'                      col_date = "date",
-#'                      year_max = 2025,
-#'                      preserve_measurements = TRUE,
-#'                      minimum = 0)
-#'
-#' # create nice plots with ggplot
-#' if (!require(ggplot2)) {
-#'
-#'   data <- septic_patients %>%
-#'     filter(mo == "ESCCOL") %>%
-#'     resistance_predict(col_ab = "amox",
-#'                       col_date = "date",
-#'                       info = FALSE,
-#'                       minimum = 15)
-#'
-#'   ggplot(data,
-#'          aes(x = year)) +
-#'     geom_col(aes(y = value),
-#'              fill = "grey75") +
-#'     geom_errorbar(aes(ymin = se_min,
-#'                       ymax = se_max),
-#'                   colour = "grey50") +
-#'     scale_y_continuous(limits = c(0, 1),
-#'                        breaks = seq(0, 1, 0.1),
-#'                        labels = paste0(seq(0, 100, 10), "%")) +
-#'     labs(title = expression(paste("Forecast of amoxicillin resistance in ",
-#'                                   italic("E. coli"))),
-#'          y = "%IR",
-#'          x = "Year") +
-#'     theme_minimal(base_size = 13)
-#' }
-resistance_predict <- function(tbl,
-                               col_ab,
-                               col_date,
-                               year_min = NULL,
-                               year_max = NULL,
-                               year_every = 1,
-                               minimum = 30,
-                               model = 'binomial',
-                               I_as_R = TRUE,
-                               preserve_measurements = TRUE,
-                               info = TRUE) {
-
-  if (nrow(tbl) == 0) {
-    stop('This table does not contain any observations.')
-  }
-
-  if (!col_ab %in% colnames(tbl)) {
-    stop('Column ', col_ab, ' not found.')
-  }
-
-  if (!col_date %in% colnames(tbl)) {
-    stop('Column ', col_date, ' not found.')
-  }
-  if ('grouped_df' %in% class(tbl)) {
-    # no grouped tibbles please, mutate will throw errors
-    tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
-  }
-
-  if (I_as_R == TRUE) {
-    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
-  }
-
-  if (!tbl %>% pull(col_ab) %>% is.rsi()) {
-    tbl[, col_ab] <- tbl %>% pull(col_ab) %>% as.rsi()
-  }
-
-  year <- function(x) {
-    if (all(grepl('^[0-9]{4}$', x))) {
-      x
-    } else {
-      as.integer(format(as.Date(x), '%Y'))
-    }
-  }
-
-  df <- tbl %>%
-    mutate(year = tbl %>% pull(col_date) %>% year()) %>%
-    group_by_at(c('year', col_ab)) %>%
-    summarise(n())
-
-  if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
-    stop("No variety in antimicrobial interpretations - all isolates are '",
-         df %>% pull(col_ab) %>% unique() %>% .[!is.na(.)], "'.",
-         call. = FALSE)
-  }
-
-  colnames(df) <- c('year', 'antibiotic', 'observations')
-  df <- df %>%
-    filter(!is.na(antibiotic)) %>%
-    tidyr::spread(antibiotic, observations, fill = 0) %>%
-    mutate(total = R + S) %>%
-    filter(total >= minimum)
-
-  if (NROW(df) == 0) {
-    stop('There are no observations.')
-  }
-
-  year_lowest <- min(df$year)
-  if (is.null(year_min)) {
-    year_min <- year_lowest
-  } else {
-    year_min <- max(year_min, year_lowest, na.rm = TRUE)
-  }
-  if (is.null(year_max)) {
-    year_max <- year(Sys.Date()) + 15
-  }
-
-  years_predict <- seq(from = year_min, to = year_max, by = year_every)
-
-  if (model %in% c('binomial', 'binom', 'logit')) {
-    logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
-    if (info == TRUE) {
-      cat('\nLogistic regression model (logit) with binomial distribution')
-      cat('\n------------------------------------------------------------\n')
-      print(summary(logitmodel))
-    }
-
-    predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else if (model == 'loglin') {
-    loglinmodel <- with(df, glm(R ~ year, family = poisson))
-    if (info == TRUE) {
-      cat('\nLog-linear regression model (loglin) with poisson distribution')
-      cat('\n--------------------------------------------------------------\n')
-      print(summary(loglinmodel))
-    }
-
-    predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else if (model %in% c('lin', 'linear')) {
-    linmodel <- with(df, lm((R / (R + S)) ~ year))
-    if (info == TRUE) {
-      cat('\nLinear regression model')
-      cat('\n-----------------------\n')
-      print(summary(linmodel))
-    }
-
-    predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-
-  } else {
-    stop('No valid model selected.')
-  }
-
-  # prepare the output dataframe
-  prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)
-
-  prediction$se_min <- prediction$value - se
-  prediction$se_max <- prediction$value + se
-
-  if (model == 'loglin') {
-    prediction$value <- prediction$value %>%
-      format(scientific = FALSE) %>%
-      as.integer()
-    prediction$se_min <- prediction$se_min %>% as.integer()
-    prediction$se_max <- prediction$se_max %>% as.integer()
-
-    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
-  } else {
-    prediction$se_max[which(prediction$se_max > 1)] <- 1
-  }
-  prediction$se_min[which(prediction$se_min < 0)] <- 0
-  prediction$observations = NA
-
-  total <- prediction
-
-  if (preserve_measurements == TRUE) {
-    # replace estimated data by observed data
-    if (I_as_R == TRUE) {
-      if (!'I' %in% colnames(df)) {
-        df$I <- 0
-      }
-      df$value <- df$R / rowSums(df[, c('R', 'S', 'I')])
-    } else {
-      df$value <- df$R / rowSums(df[, c('R', 'S')])
-    }
-    measurements <- data.frame(year = df$year,
-                               value = df$value,
-                               se_min = NA,
-                               se_max = NA,
-                               observations = df$total,
-                               stringsAsFactors = FALSE)
-    colnames(measurements) <- colnames(prediction)
-
-    total <- rbind(measurements,
-                   prediction %>% filter(!year %in% df$year))
-    if (model %in% c('binomial', 'binom', 'logit')) {
-      total <- total %>% mutate(observed = ifelse(is.na(observations), NA, value),
-                                estimated = prediction$value)
-    }
-  }
-
-  if ("value" %in% colnames(total)) {
-    total <- total %>%
-      mutate(value = case_when(value > 1 ~ 1,
-                               value < 0 ~ 0,
-                               TRUE ~ value))
-  }
-
-  total %>% arrange(year)
-
-}
-
-#' @rdname resistance_predict
-#' @export
-rsi_predict <- resistance_predict
@@ -1,203 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' Class 'rsi'
-#'
-#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
-#' @rdname as.rsi
-#' @param x vector
-#' @details The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
-#' @return Ordered factor with new class \code{rsi}
-#' @keywords rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @seealso \code{\link{as.mic}}
-#' @examples
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
-#' is.rsi(rsi_data)
-#'
-#' # this can also coerce combined MIC/RSI values:
-#' as.rsi("<= 0.002; S") # will return S
-#'
-#' plot(rsi_data)    # for percentages
-#' barplot(rsi_data) # for frequencies
-#' freq(rsi_data)    # frequency table with informative header
-#'
-#' # fastest way to transform all columns with already valid AB results to class `rsi`:
-#' library(dplyr)
-#' septic_patients %>%
-#'   mutate_if(is.rsi.eligible,
-#'             as.rsi)
-as.rsi <- function(x) {
-  if (is.rsi(x)) {
-    x
-  } else {
-
-    x <- x %>% unlist()
-    x.bak <- x
-
-    na_before <- x[is.na(x) | x == ''] %>% length()
-    # remove all spaces
-    x <- gsub(' +', '', x)
-    # remove all MIC-like values: numbers, operators and periods
-    x <- gsub('[0-9.,;:<=>]+', '', x)
-    # disallow more than 3 characters
-    x[nchar(x) > 3] <- NA
-    # set to capitals
-    x <- toupper(x)
-    # remove all invalid characters
-    x <- gsub('[^RSI]+', '', x)
-    # in cases of "S;S" keep S, but in case of "S;I" make it NA
-    x <- gsub('^S+$', 'S', x)
-    x <- gsub('^I+$', 'I', x)
-    x <- gsub('^R+$', 'R', x)
-    x[!x %in% c('S', 'I', 'R')] <- NA
-    na_after <- x[is.na(x) | x == ''] %>% length()
-
-    if (na_before != na_after) {
-      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
-        unique() %>%
-        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 antimicrobial interpretations: ',
-              list_missing, call. = FALSE)
-    }
-
-    x <- factor(x, levels = c("S", "I", "R"), ordered = TRUE)
-    class(x) <- c('rsi', 'ordered', 'factor')
-    x
-  }
-}
-
-#' @rdname as.rsi
-#' @export
-#' @importFrom dplyr %>%
-is.rsi <- function(x) {
-  class(x) %>% identical(c('rsi', 'ordered', 'factor'))
-}
-
-#' @rdname as.rsi
-#' @export
-#' @importFrom dplyr %>%
-is.rsi.eligible <- function(x) {
-  if (is.logical(x)
-      | is.numeric(x)
-      | is.mo(x)
-      | identical(class(x), "Date")
-      | identical(levels(x), c("S", "I", "R"))) {
-    # no transformation needed
-    FALSE
-  } else {
-    # check all but a-z
-    x <- unique(gsub("[^RSIrsi]+", "", unique(x)))
-    all(x %in% c("R", "I", "S", "", NA_character_)) &
-      !all(x %in% c("", NA_character_))
-  }
-}
-
-#' @exportMethod print.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-print.rsi <- function(x, ...) {
-  cat("Class 'rsi'\n")
-  print(as.character(x), quote = FALSE)
-}
-
-#' @exportMethod summary.rsi
-#' @export
-#' @noRd
-summary.rsi <- function(object, ...) {
-  x <- object
-  c(
-    "Mode" = 'rsi',
-    "<NA>" = sum(is.na(x)),
-    "Sum S" = sum(x == "S", na.rm = TRUE),
-    "Sum IR" = sum(x %in% c("I", "R"), na.rm = TRUE),
-    "-Sum R" = sum(x == "R", na.rm = TRUE),
-    "-Sum I" = sum(x == "I", na.rm = TRUE)
-  )
-}
-
-#' @exportMethod plot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics plot text
-#' @noRd
-plot.rsi <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-
-  data <- data.frame(x = x,
-                     y = 1,
-                     stringsAsFactors = TRUE) %>%
-    group_by(x) %>%
-    summarise(n = sum(y)) %>%
-    filter(!is.na(x)) %>%
-    mutate(s = round((n / sum(n)) * 100, 1))
-  data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
-
-  ymax <- if_else(max(data$s) > 95, 105, 100)
-
-  plot(x = data$x,
-       y = data$s,
-       lwd = 2,
-       col = c('green', 'orange', 'red'),
-       ylim = c(0, ymax),
-       ylab = 'Percentage',
-       xlab = 'Antimicrobial Interpretation',
-       main = paste('Susceptibility Analysis of', x_name),
-       axes = FALSE,
-       ...)
-  # x axis
-  axis(side = 1, at = 1:n_distinct(data$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$n, ')'))
-}
-
-
-#' @exportMethod barplot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
-#' @importFrom graphics barplot axis
-#' @noRd
-barplot.rsi <- function(height, ...) {
-  x <- height
-  x_name <- deparse(substitute(height))
-
-  data <- data.frame(rsi = x, cnt = 1) %>%
-    group_by(rsi) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-
-  barplot(table(x),
-          col = c('green3', 'orange2', 'red3'),
-          xlab = 'Antimicrobial Interpretation',
-          main = paste('Susceptibility Analysis of', x_name),
-          ylab = 'Frequency',
-          axes = FALSE,
-          ...)
-  # y axis, 0-100%
-  axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
-}
@@ -1,144 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' @importFrom dplyr %>% pull all_vars any_vars filter_all funs mutate_all
-rsi_calc <- function(...,
-                     type,
-                     include_I,
-                     minimum,
-                     as_percent,
-                     also_single_tested,
-                     only_count) {
-
-  if (!is.logical(include_I)) {
-    stop('`include_I` must be logical', call. = FALSE)
-  }
-  if (!is.numeric(minimum)) {
-    stop('`minimum` must be numeric', call. = FALSE)
-  }
-  if (!is.logical(as_percent)) {
-    stop('`as_percent` must be logical', call. = FALSE)
-  }
-  if (!is.logical(also_single_tested)) {
-    stop('`also_single_tested` must be logical', call. = FALSE)
-  }
-
-  dots_df <- ...elt(1) # it needs this evaluation
-  dots <- base::eval(base::substitute(base::alist(...)))
-  ndots <- length(dots)
-
- if ("data.frame" %in% class(dots_df)) {
-   # data.frame passed with other columns, like:
-   #   septic_patients %>% portion_S(amcl, gent)
-   dots <- as.character(dots)
-   dots <- dots[dots != "."]
-    if (length(dots) == 0 | all(dots == "df")) {
-      # for complete data.frames, like septic_patients %>% select(amcl, gent) %>% portion_S()
-      # and the old rsi function, that has "df" as name of the first parameter
-      x <- dots_df
-    } else {
-      x <- dots_df[, dots]
-    }
-  } else if (ndots == 1) {
-    # only 1 variable passed (can also be data.frame), like:
-    #   portion_S(septic_patients$amcl)
-    #   septic_patients$amcl %>% portion_S()
-    x <- dots_df
-  } else {
-    # multiple variables passed without pipe, like:
-    #   portion_S(septic_patients$amcl, septic_patients$gent)
-    x <- NULL
-    try(x <- as.data.frame(dots), silent = TRUE)
-    if (is.null(x)) {
-      # support for: with(septic_patients, portion_S(amcl, gent))
-      x <- as.data.frame(rlang::list2(...))
-    }
-  }
-
-  print_warning <- FALSE
-
-  type_trans <- as.integer(as.rsi(type))
-  type_others <- setdiff(1:3, type_trans)
-
-  if (is.data.frame(x)) {
-    rsi_integrity_check <- character(0)
-    for (i in 1:ncol(x)) {
-      # check integrity of columns: force rsi class
-      if (!is.rsi(x %>% pull(i))) {
-        rsi_integrity_check <- c(rsi_integrity_check, x %>% pull(i) %>% as.character())
-        x[, i] <- suppressWarnings(as.rsi(x[, i])) # warning will be given later
-        print_warning <- TRUE
-      }
-      x[, i] <- x %>% pull(i) %>% as.integer()
-    }
-    if (length(rsi_integrity_check) > 0) {
-      # this will give a warning for invalid results, of all input columns (so only 1 warning)
-      rsi_integrity_check <- as.rsi(rsi_integrity_check)
-    }
-
-    if (include_I == TRUE) {
-      x <- x %>% mutate_all(funs(ifelse(. == 2, type_trans, .)))
-    }
-
-    if (also_single_tested == TRUE) {
-      # THE CHANCE THAT AT LEAST ONE RESULT IS type
-      found <- x %>% filter_all(any_vars(. == type_trans)) %>% nrow()
-      # THE CHANCE THAT AT LEAST ONE RESULT IS type OR ALL ARE TESTED
-      total <- found + x %>% filter_all(all_vars(. %in% type_others)) %>% nrow()
-    } else {
-      x <- apply(X = x,
-                 MARGIN = 1,
-                 FUN = min)
-      found <- sum(as.integer(x) == type_trans, na.rm = TRUE)
-      total <- length(x) - sum(is.na(x))
-    }
-  } else {
-    if (!is.rsi(x)) {
-      x <- as.rsi(x)
-      print_warning <- TRUE
-    }
-    x <- as.integer(x)
-    if (include_I == TRUE) {
-      x[x == 2] <- type_trans
-    }
-    found <- sum(x == type_trans, na.rm = TRUE)
-    total <- length(x) - sum(is.na(x))
-  }
-
-  if (print_warning == TRUE) {
-    warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
-            call. = FALSE)
-  }
-
-  if (only_count == TRUE) {
-    return(found)
-  }
-
-  if (total < minimum) {
-    warning("Introducing NA: only ", total, " results available (minimum set to ", minimum, ").", call. = FALSE)
-    result <- NA
-  } else {
-    result <- found / total
-  }
-
-  if (as_percent == TRUE) {
-    percent(result, force_zero = TRUE)
-  } else {
-    result
-  }
-}
@@ -1,40 +0,0 @@
-#' 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: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
-#' @param x a vector of values, a \code{matrix} or a \code{data frame}
-#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
-#' @exportMethod skewness
-#' @seealso \code{\link{kurtosis}}
-#' @rdname skewness
-#' @export
-skewness <- function(x, na.rm = FALSE) {
-  UseMethod("skewness")
-}
-
-#' @exportMethod skewness.default
-#' @rdname skewness
-#' @export
-skewness.default <- function (x, na.rm = FALSE) {
-  x <- as.vector(x)
-  if (na.rm == TRUE) {
-    x <- x[!is.na(x)]
-  }
-  n <- length(x)
-  (base::sum((x - base::mean(x))^3) / n) / (base::sum((x - base::mean(x))^2) / n)^(3/2)
-}
-
-#' @exportMethod skewness.matrix
-#' @rdname skewness
-#' @export
-skewness.matrix <- function (x, na.rm = FALSE) {
-  base::apply(x, 2, skewness.default, na.rm = na.rm)
-}
-
-#' @exportMethod skewness.data.frame
-#' @rdname skewness
-#' @export
-skewness.data.frame <- function (x, na.rm = FALSE) {
-  base::sapply(x, skewness.default, na.rm = na.rm)
-}
@@ -1,49 +0,0 @@
-# ==================================================================== #
-# TITLE                                                                #
-# Antimicrobial Resistance (AMR) Analysis                              #
-#                                                                      #
-# AUTHORS                                                              #
-# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
-#                                                                      #
-# LICENCE                                                              #
-# This program is free software; you can redistribute it and/or modify #
-# it under the terms of the GNU General Public License version 2.0,    #
-# as published by the Free Software Foundation.                        #
-#                                                                      #
-# This program is distributed in the hope that it will be useful,      #
-# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
-# GNU General Public License for more details.                         #
-# ==================================================================== #
-
-#' The \code{AMR} Package
-#'
-#' Welcome to the \code{AMR} package. This page gives some additional contact information about the authors.
-#' @details
-#' This package was intended to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and work with antibiotic properties by using evidence-based methods.
-#'
-#' This package was created for academic research by PhD students of the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG).
-#' @section Authors:
-#' Matthijs S. Berends[1,2] Christian F. Luz[1], Erwin E.A. Hassing[2],  Corinna Glasner[1],  Alex W. Friedrich[1],  Bhanu Sinha[1] \cr
-#'
-#' [1] Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - \url{rug.nl} \url{umcg.nl} \cr
-#' [2] Certe Medical Diagnostics & Advice, Groningen, the Netherlands - \url{certe.nl}
-#' @section Contact us:
-#' For suggestions, comments or questions, please contact us at:
-#'
-#' Matthijs S. Berends \cr
-#' m.s.berends [at] umcg [dot] nl \cr
-#' Department of Medical Microbiology, University of Groningen \cr
-#' University Medical Center Groningen \cr
-#' Post Office Box 30001 \cr
-#' 9700 RB Groningen
-#'
-#' If you have found a bug, please file a new issue at: \cr
-#' \url{https://gitlab.com/msberends/AMR/issues}
-#' @name AMR
-#' @rdname AMR
-NULL
-
-.onLoad <- function(libname, pkgname) {
-  backports::import(pkgname)
-}
@@ -1,646 +0,0 @@
-# `AMR` <img src="man/figures/logo_amr.png" align="right" height="120px" />
-### An [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with antibiotic properties by using evidence-based methods.
-
-This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the Medical Microbiology & Infection Prevention (MMBI) department of the [University Medical Center Groningen (UMCG)](https://www.umcg.nl).
-
-:arrow_forward: Get it with `install.packages("AMR")` or see below for other possibilities.
-
-:arrow_forward: Read the [changelog here](https://gitlab.com/msberends/AMR/blob/master/NEWS.md).
-
-## Authors
-Matthijs S. Berends <a href="https://orcid.org/0000-0001-7620-1800"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,2,a</sup>,
-Christian F. Luz <a href="https://orcid.org/0000-0001-5809-5995"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,a</sup>,
-Erwin E.A. Hassing<sup>2</sup>,
-Corinna Glasner <a href="https://orcid.org/0000-0003-1241-1328"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>,
-Alex W. Friedrich <a href="https://orcid.org/0000-0003-4881-038X"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>,
-Bhanu Sinha <a href="https://orcid.org/0000-0003-1634-0010"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> <sup>1,b</sup>
-  
-<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - [rug.nl](http://www.rug.nl) [umcg.nl](http://www.umcg.nl)<br>
-<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands - [certe.nl](http://www.certe.nl)<br>
-<sup>a</sup> R package author and thesis dissertant<br>
-<sup>b</sup> Thesis advisor
-
-<a href="https://www.rug.nl"><img src="man/figures/logo_rug.png" height="60px"></a>
-<a href="https://www.umcg.nl"><img src="man/figures/logo_umcg.png" height="60px"></a>
-<a href="https://www.certe.nl"><img src="man/figures/logo_certe.png" height="60px"></a>
-<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_eh1h.png" height="60px"></a>
-<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_interreg.png" height="60px"></a>
-
-## Contents
-* [Why this package?](#why-this-package)
-  * [ITIS](#itis)
-* [How to get it?](#how-to-get-it)
-  * [Install from CRAN](#install-from-cran)
-  * [Install from Zenodo](#install-from-zenodo)
-  * [Install from GitLab](#install-from-gitlab)
-* [How to use it?](#how-to-use-it)
-  * [New classes](#new-classes)
-  * [Overwrite/force resistance based on EUCAST rules](#overwriteforce-resistance-based-on-eucast-rules)
-  * [Other (microbial) epidemiological functions](#other-microbial-epidemiological-functions)
-  * [Frequency tables](#frequency-tables)
-  * [Data sets included in package](#data-sets-included-in-package)
-* [Benchmarks](#benchmarks)
-* [Copyright](#copyright)
-
-## Why this package?
-This R package was intended **to make microbial epidemiology easier**. Most functions contain extensive help pages to get started.
-
-The `AMR` package basically does four important things:
-
-1. It **cleanses existing data**, by transforming it to reproducible and profound *classes*, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:
-
-   * Use `as.mo` to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AUR". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" and "esccol". Even `as.mo("MRSA")` will return the ID of *S. aureus*. Moreover, it can group all coagulase negative and positive *Staphylococci*, and can transform *Streptococci* into Lancefield groups. To find bacteria based on your input, it uses Artificial Intelligence to look up values in the included ITIS data, consisting of more than 18,000 microorganisms. It is *very* fast, see [Benchmarks](#benchmarks).
-   * Use `as.rsi` to transform values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
-   * Use `as.mic` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
-   * Use `as.atc` to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.
-   
-2. It **enhances existing data** and **adds new data** from data sets included in this package.
-
-   * Use `EUCAST_rules` to apply [EUCAST expert rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/).
-   * Use `first_isolate` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
-     * You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
-   * Use `MDRO` (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
-   * The data set `microorganisms` contains the complete taxonomic tree of more than 18,000 microorganisms (bacteria, fungi/yeasts and protozoa). Furthermore, the colloquial name and Gram stain are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus`, `mo_family`, `mo_gramstain` or even `mo_phylum`. As they use `as.mo` internally, they also use artificial intelligence. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
-   * The data set `antibiotics` contains the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like `ab_name` and `ab_tradenames` to look up values. The `ab_*` functions use `as.atc` internally so they support AI to guess your expected result. For example, `ab_name("Fluclox")`, `ab_name("Floxapen")` and `ab_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
-
-3. It **analyses the data** with convenient functions that use well-known methods.
-
-   * Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` functions. Similarly, the *number* of isolates can be determined with the `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` functions. All these functions can be used [with the `dplyr` package](https://dplyr.tidyverse.org/#usage) (e.g. in conjunction with [`summarise`](https://dplyr.tidyverse.org/reference/summarise.html))
-   * Plot AMR results with `geom_rsi`, a function made for the `ggplot2` package
-   * Predict antimicrobial resistance for the nextcoming years using logistic regression models with the `resistance_predict` function
-   * Conduct descriptive statistics to enhance base R: calculate kurtosis, skewness and create frequency tables
-
-4. It **teaches the user** how to use all the above actions.
-
-   * The package contains extensive help pages with many examples.
-   * It also contains an example data set called `septic_patients`. This data set contains:
-     * 2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands
-     * Results of 40 antibiotics (each antibiotic in its own column) with a total of 38,414 antimicrobial results
-     * Real and genuine data
-     
-### ITIS
-<img src="man/figures/itis_logo.jpg" height="100px">
-
-This package contains the **complete microbial taxonomic data** (with all eight taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov). 
-
-All (sub)species from **the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package**, as well as all previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.
-
-ITIS is a partnership of U.S., Canadian, and Mexican agencies and taxonomic specialists.
-
-**Get a note when a species was renamed**
-```r
-mo_shortname("Chlamydia psittaci")
-# Note: 'Chlamydia psittaci' (Page, 1968) was renamed 'Chlamydophila psittaci' (Everett et al., 1999)
-# [1] "C. psittaci"
-```
-
-**Get any property from the entire taxonomic tree for all included species**
-```r
-mo_class("E. coli")
-# [1] "Gammaproteobacteria"
-
-mo_family("E. coli")
-# [1] "Enterobacteriaceae"
-
-mo_subkingdom("E. coli")
-# [1] "Negibacteria"
-
-mo_gramstain("E. coli") # based on subkingdom
-# [1] "Gram negative"
-
-mo_ref("E. coli")
-# [1] "Castellani and Chalmers, 1919"
-```
-
-**Do not get mistaken - the package only includes microorganisms**
-```r
-mo_phylum("C. elegans")
-# [1] "Cyanobacteria"                   # Bacteria?!
-mo_fullname("C. elegans")
-# [1] "Chroococcus limneticus elegans"  # Because a microorganism was found 
-```
-
-## How to get it?
-All stable versions of this package [are published on CRAN](http://cran.r-project.org/package=AMR), the official R network with a peer-reviewed submission process.
-
-### Install from CRAN
-[![CRAN_Badge](https://www.r-pkg.org/badges/version/AMR)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
-
-(Note: Downloads measured only by [cran.rstudio.com](https://cran.rstudio.com/package=AMR), this excludes e.g. the official [cran.r-project.org](https://cran.r-project.org/package=AMR))
-
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> Install using [RStudio](http://www.rstudio.com) (recommended):
-  - Click on `Tools` and then `Install Packages...`
-  - Type in `AMR` and press <kbd>Install</kbd>
-
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly:
-  - `install.packages("AMR")`
-
-### Install from Zenodo
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1305355.svg)](https://doi.org/10.5281/zenodo.1305355)
-
-This package was also published on Zenodo (stable releases only): https://doi.org/10.5281/zenodo.1305355
-
-### Install from GitLab
-
-This is the latest **development version**. Although it may contain bugfixes and even new functions compared to the latest released version on CRAN, it is also subject to change and may be unstable or behave unexpectedly. Always consider this a beta version. All below 'badges' should be green:
-
-Development Test | Result | Reference
--- | :---: | ---
-All functions checked on Linux | [![pipeline status](https://gitlab.com/msberends/AMR/badges/master/pipeline.svg)](https://gitlab.com/msberends/AMR/commits/master) | GitLab CI [[ref 1]](https://gitlab.com/msberends/AMR/pipelines) 
-All functions checked on Windows | [![AppVeyor_Build](https://ci.appveyor.com/api/projects/status/gitlab/msberends/AMR?branch=master&svg=true)](https://ci.appveyor.com/project/msberends/amr-svxon) | Appveyor Systems Inc. [[ref 2]](https://ci.appveyor.com/project/msberends/amr-svxon)
-Percentage of syntax lines checked | [![Code_Coverage](https://codecov.io/gl/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gl/msberends/AMR) | Codecov LLC [[ref 3]](https://codecov.io/gl/msberends/AMR)
-
-If so, try it with:
-```r
-install.packages("devtools") 
-devtools::install_gitlab("msberends/AMR")
-```
-
-## How to use it?
-```r
-# Call it with:
-library(AMR)
-
-# For a list of functions:
-help(package = "AMR")
-```
-
-### New classes
-This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
-Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
-
-These functions also try to coerce valid values.
-
-#### RSI
-The `septic_patients` data set comes with antimicrobial results of more than 40 different drugs. For example, columns `amox` and `cipr` contain results of amoxicillin and ciprofloxacin, respectively.
-```r
-summary(septic_patients[, c("amox", "cipr")])
-#      amox          cipr     
-#  Mode  :rsi    Mode  :rsi   
-#  <NA>  :1002   <NA>  :596   
-#  Sum S :336    Sum S :1108  
-#  Sum IR:662    Sum IR:296   
-#  -Sum R:659    -Sum R:227   
-#  -Sum I:3      -Sum I:69  
-```
-
-You can use the `plot` function from base R:
-```r
-plot(septic_patients$cipr)
-```
-
-![example_1_rsi](man/figures/rsi_example1.png)
-
-Or use the `ggplot2` and `dplyr` packages to create more appealing plots:
-
-```r
-library(dplyr)
-library(ggplot2)
-
-septic_patients %>%
-  select(amox, nitr, fosf, trim, cipr) %>%
-  ggplot_rsi()
-```
-
-![example_2_rsi](man/figures/rsi_example2.png)
-
-Adjust it with any parameter you know from the `ggplot2` package:
-
-```r
-septic_patients %>%
-  select(amox, nitr, fosf, trim, cipr) %>%
-  ggplot_rsi(datalabels = FALSE, 
-             width = 0.5, colour = "purple", size = 1, linetype = 2, alpha = 0.5)
-```
-
-![example_3_rsi](man/figures/rsi_example3.png)
-
-It also supports grouping variables. Let's say we want to compare resistance of drugs against Urine Tract Infections (UTI) between hospitals A to D (variable `hospital_id`):
-
-```r
-septic_patients %>%
-  select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
-  group_by(hospital_id) %>%
-  ggplot_rsi(x = "hospital_id",
-             facet = "Antibiotic",
-             nrow = 1,
-             datalabels = FALSE) +
-  labs(title = "AMR of Anti-UTI Drugs Per Hospital",
-       x = "Hospital")
-```
-
-![example_4_rsi](man/figures/rsi_example4.png)
-
-You could use this to group on anything in your plots: Gram stain, age (group), genus, geographic location, et cetera.
-
-Is there a significant difference between hospital A and D when it comes to Fosfomycin?
-```r
-check_A_and_D <- septic_patients %>%
-  filter(hospital_id %in% c("A", "D")) %>% # filter on only hospitals A and D
-  select(hospital_id, fosf) %>%            # select the hospitals and fosfomycin
-  group_by(hospital_id) %>%
-  count_df(combine_IR = TRUE) %>%          # count all isolates per group (hospital_id)
-  tidyr::spread(hospital_id, Value) %>%    # transform output so A and D are columns
-  select(A, D) %>%                         # and select these only
-  as.matrix()                              # transform to good old matrix for fisher.test
-
-check_A_and_D
-#       A  D
-# [1,] 24 33
-# [2,] 25 77
-```
-
-Total sum is lower than 1,000 so we'd prefer a [Fisher's exact test](https://en.wikipedia.org/wiki/Fisher%27s_exact_test), not a [*G*-test](https://en.wikipedia.org/wiki/G-test) (or its formerly used equivalent, the famous [Chi<sup>2</sup> test](https://en.wikipedia.org/wiki/Chi-squared_test)):
-```r
-fisher.test(check_A_and_D)
-# 
-# 	Fisher's Exact Test for Count Data
-# 
-# data:  check_A_and_D
-# p-value = 0.03104
-# alternative hypothesis: true odds ratio is not equal to 1
-# 95 percent confidence interval:
-#  1.054283 4.735995
-# sample estimates:
-# odds ratio 
-#   2.228006 
-```
-
-Well, there you go!
-
-#### MIC
-
-```r
-# Transform values to new class
-mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
-
-summary(mic_data)
-#  Mode:mic      
-#  <NA>:0        
-#  Min.:<=0.128  
-#  Max.:>=32 
-
-plot(mic_data)
-```
-![example_mic](man/figures/mic_example.png)
-
-
-### Overwrite/force resistance based on EUCAST rules
-This is also called *interpretive reading*.
-```r
-a <- data.frame(mo = c("Staphylococcus aureus",
-                       "Enterococcus faecalis",
-                       "Escherichia coli",
-                       "Klebsiella pneumoniae",
-                       "Pseudomonas aeruginosa"),
-                vanc = "-",       # Vancomycin
-                amox = "-",       # Amoxicillin
-                coli = "-",       # Colistin
-                cfta = "-",       # Ceftazidime
-                cfur = "-",       # Cefuroxime
-                peni = "S",       # Benzylpenicillin
-                cfox = "S",       # Cefoxitin
-                stringsAsFactors = FALSE)
-                
-a
-#                       mo vanc amox coli cfta cfur peni cfox
-# 1  Staphylococcus aureus    -    -    -    -    -    S    S
-# 2  Enterococcus faecalis    -    -    -    -    -    S    S
-# 3       Escherichia coli    -    -    -    -    -    S    S
-# 4  Klebsiella pneumoniae    -    -    -    -    -    S    S
-# 5 Pseudomonas aeruginosa    -    -    -    -    -    S    S
-
-b <- EUCAST_rules(a) # 18 results are forced as R or S
-
-b
-#                       mo vanc amox coli cfta cfur peni cfox
-# 1  Staphylococcus aureus    -    S    R    R    S    S    S
-# 2  Enterococcus faecalis    -    -    R    R    R    S    R
-# 3       Escherichia coli    R    -    -    -    -    R    S
-# 4  Klebsiella pneumoniae    R    R    -    -    -    R    S
-# 5 Pseudomonas aeruginosa    R    R    -    -    R    R    R
-```
-
-Bacteria IDs can be retrieved with the `guess_mo` function. It uses any type of info about a microorganism as input. For example, all these will return value `B_STPHY_AUR`, the ID of *S. aureus*:
-```r
-guess_mo("stau")
-guess_mo("STAU")
-guess_mo("staaur")
-guess_mo("S. aureus")
-guess_mo("S aureus")
-guess_mo("Staphylococcus aureus")
-guess_mo("MRSA") # Methicillin Resistant S. aureus
-guess_mo("MSSA") # Methicillin Susceptible S. aureus
-guess_mo("VISA") # Vancomycin Intermediate S. aureus
-guess_mo("VRSA") # Vancomycin Resistant S. aureus
-```
-
-### Other (microbial) epidemiological functions
-
-```r
-# G-test to replace Chi squared test
-g.test(...)
-
-# Determine key antibiotic based on bacteria ID
-key_antibiotics(...)
-
-# Selection of first isolates of any patient
-first_isolate(...)
-
-# Predict resistance levels of antibiotics
-resistance_predict(...)
-
-# Get name of antibiotic by ATC code
-abname(...)
-abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
-```
-
-### Frequency tables
-Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways:
-```r
-# Like base R:
-freq(mydata$myvariable)
-
-# And like tidyverse:
-mydata %>% freq(myvariable)
-```
-
-Frequency are of course sorted by count at default:
-```r
-septic_patients %>% freq(hospital_id)
-# Class:     factor (numeric)
-# Length:    2000 (of which NA: 0 = 0.00%)
-# Unique:    4
-# 
-#      Item    Count   Percent   Cum. Count   Cum. Percent
-# ---  -----  ------  --------  -----------  -------------
-# 1    D         762     38.1%          762          38.1%
-# 2    B         663     33.1%         1425          71.2%
-# 3    A         321     16.1%         1746          87.3%
-# 4    C         254     12.7%         2000         100.0%
-```
-
-This can be changed with the `sort.count` parameter:
-```r
-septic_patients %>% freq(hospital_id, sort.count = FALSE)
-# Class:     factor (numeric)
-# Length:    2000 (of which NA: 0 = 0.00%)
-# Unique:    4
-# 
-#      Item    Count   Percent   Cum. Count   Cum. Percent
-# ---  -----  ------  --------  -----------  -------------
-# 1    A         321     16.1%          321          16.1%
-# 2    B         663     33.1%          984          49.2%
-# 3    C         254     12.7%         1238          61.9%
-# 4    D         762     38.1%         2000         100.0%
-```
-
-For numeric values, some extra descriptive statistics will be calculated:
-```r
-freq(runif(n = 10, min = 1, max = 5))
-# Frequency table  
-# Class:     numeric
-# Length:    10 (of which NA: 0 = 0.00%)
-# Unique:    10
-# 
-# Mean:      3.1
-# Std. dev.: 1.3 (CV: 0.43, MAD: 1.8)
-# Five-Num:  1.3 | 1.7 | 3.2 | 4.3 | 5.0 (IQR: 2.6, CQV: 0.43)
-# Outliers:  0
-# 
-#           Item   Count   Percent   Cum. Count   Cum. Percent
-# ---  ---------  ------  --------  -----------  -------------
-# 1     1.271079       1     10.0%            1          10.0%
-# 2     1.333975       1     10.0%            2          20.0%
-# 3     1.714946       1     10.0%            3          30.0%
-# 4     2.751871       1     10.0%            4          40.0%
-# 5     3.090140       1     10.0%            5          50.0%
-# 6     3.260850       1     10.0%            6          60.0%
-# 7     3.824105       1     10.0%            7          70.0%
-# 8     4.278028       1     10.0%            8          80.0%
-# 9     4.436265       1     10.0%            9          90.0%
-# 10    4.996694       1     10.0%           10         100.0%
-# 
-# Warning message:
-# All observations are unique. 
-```
-Learn more about this function with:
-```r
-?freq
-```
-
-### Data sets included in package
-Data sets to work with antibiotics and bacteria properties.
-```r
-# Data set with complete taxonomic trees from ITIS, containing of 
-# the three kingdoms Bacteria, Fungi and Protozoa
-microorganisms     # data.frame: 18,833 x 15
-microorganisms.old # data.frame: 2,383 x 4
-
-# Data set with ATC antibiotics codes, official names, trade names 
-# and DDDs (oral and parenteral)
-antibiotics       #  data.frame: 423 x 18
-
-# Data set with 2000 random blood culture isolates from anonymised
-# septic patients between 2001 and 2017 in 5 Dutch hospitals
-septic_patients    # data.frame: 2,000 x 49
-
-```
-
-## Benchmarks
-
-One of the most important features of this package is the complete microbial taxonomic database, supplied by ITIS (https://www.itis.gov). We created a function `as.mo` that transforms any user input value to a valid microbial ID by using AI (Artificial Intelligence) and based on the taxonomic tree of ITIS. 
-
-Using the `microbenchmark` package, we can review the calculation performance of this function.
-
-```r
-library(microbenchmark)
-```
-
-In the next test, we try to 'coerce' different input values for *Staphylococcus aureus*. The actual result is the same every time: it returns its MO code `B_STPHY_AUR` (*B* stands for *Bacteria*, the taxonomic kingdom). 
-
-But the calculation time differs a lot. Here, the AI effect can be reviewed best:
-
-```r
-microbenchmark(A = as.mo("stau"),
-               B = as.mo("staaur"),
-               C = as.mo("S. aureus"),
-               D = as.mo("S.  aureus"),
-               E = as.mo("STAAUR"),
-               F = as.mo("Staphylococcus aureus"),
-               G = as.mo("B_STPHY_AUR"),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr       min        lq       mean     median        uq       max neval
-#     A 34.745551 34.798630 35.2596102 34.8994810 35.258325 38.067062    10
-#     B  7.095386  7.125348  7.2219948  7.1613865  7.240377  7.495857    10
-#     C 11.677114 11.733826 11.8304789 11.7715050 11.843756 12.317559    10
-#     D 11.694435 11.730054 11.9859313 11.8775585 12.206371 12.750016    10
-#     E  7.044402  7.117387  7.2271630  7.1923610  7.246104  7.742396    10
-#     F  6.642326  6.778446  6.8988042  6.8753165  6.923577  7.513945    10
-#     G  0.106788  0.131023  0.1351229  0.1357725  0.144014  0.146458    10
-```
-
-In the table above, all measurements are shown in milliseconds (thousands of seconds), tested on a quite regular Linux server from 2007 (Core 2 Duo 2.7 GHz, 2 GB DDR2 RAM). A value of 6.9 milliseconds means it will roughly determine 144 input values per second. It case of 39.2 milliseconds, this is only 26 input values per second. The more an input value resembles a full name (like C, D and F), the faster the result will be found. In case of G, the input is already a valid MO code, so it only almost takes no time at all (0.0001 seconds on our server).
-
-To achieve this speed, the `as.mo` function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined far less faster. See this example for the ID of *Burkholderia nodosa* (`B_BRKHL_NOD`):
-
-```r
-microbenchmark(A = as.mo("buno"),
-               B = as.mo("burnod"),
-               C = as.mo("B. nodosa"),
-               D = as.mo("B.  nodosa"),
-               E = as.mo("BURNOD"),
-               F = as.mo("Burkholderia nodosa"),
-               G = as.mo("B_BRKHL_NOD"),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr        min         lq        mean      median         uq        max neval
-#     A 124.175427 124.474837 125.8610536 125.3750560 126.160945 131.485994    10
-#     B 154.249713 155.364729 160.9077032 156.8738940 157.136183 197.315105    10
-#     C  66.066571  66.162393  66.5538611  66.4488130  66.698077  67.623404    10
-#     D  86.747693  86.918665  90.7831016  87.8149725  89.440982 116.767991    10
-#     E 154.863827 155.208563 162.6535954 158.4062465 168.593785 187.378088    10
-#     F  32.427028  32.638648  32.9929454  32.7860475  32.992813  34.674241    10
-#     G   0.213155   0.216578   0.2369226   0.2338985   0.253734   0.285581    10
-```
-
-That takes up to 11 times as much time! A value of 158.4 milliseconds means it can only determine ~6 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance.
-
-To relieve this pitfall and further improve performance, two important calculations take almost no time at all: **repetive results** and **already precalculated results**.
-
-Repetive results mean that unique values are present more than once. Unique values will only be calculated once by `as.mo`.  We will use `mo_fullname` for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) and uses `as.mo` internally.
-```r
-library(dplyr)
-# take 500,000 random MO codes from the septic_patients data set
-x = septic_patients %>%
-  sample_n(500000, replace = TRUE) %>%
-  pull(mo)
-  
-# got the right length?
-length(x)
-# [1] 500000
-
-# and how many unique values do we have?
-n_distinct(x)
-# [1] 96
-
-# only 96, but distributed in 500,000 results. now let's see:
-microbenchmark(X = mo_fullname(x),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr      min       lq     mean   median       uq      max neval
-#     X 114.9342 117.1076 129.6448 120.2047 131.5005 168.6371    10
-```
-
-So transforming 500,000 values (!) of 96 unique values only takes 0.12 seconds (120 ms). You only lose time on your unique input values.
-
-Results of a tenfold - 5,000,000 values:
-```r
-# Unit: milliseconds
-#  expr      min       lq     mean   median       uq      max neval
-#     X 882.9045 901.3011 1001.677 940.3421 1168.088 1226.846    10
-```
-
-Even the full names of 5 *Million* values are calculated within a second.
-
-What about precalculated results? If the input is an already precalculated result of a helper function like `mo_fullname`, it almost doesn't take any time at all (see 'C' below):
-
-```r
-microbenchmark(A = mo_fullname("B_STPHY_AUR"),
-               B = mo_fullname("S. aureus"),
-               C = mo_fullname("Staphylococcus aureus"),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr       min        lq       mean     median        uq       max neval
-#     A 11.364086 11.460537 11.5104799 11.4795330 11.524860 11.818263    10
-#     B 11.976454 12.012352 12.1704592 12.0853020 12.210004 12.881737    10
-#     C  0.095823  0.102528  0.1167754  0.1153785  0.132629  0.140661    10
-```
-
-So going from `mo_fullname("Staphylococcus aureus")` to `"Staphylococcus aureus"` takes 0.0001 seconds - it doesn't even start calculating *if the result would be the same as the expected resulting value*. That goes for all helper functions:
-
-```r
-microbenchmark(A = mo_species("aureus"),
-               B = mo_genus("Staphylococcus"),
-               C = mo_fullname("Staphylococcus aureus"),
-               D = mo_family("Staphylococcaceae"),
-               E = mo_order("Bacillales"),
-               F = mo_class("Bacilli"),
-               G = mo_phylum("Firmicutes"),
-               H = mo_subkingdom("Posibacteria"),
-               I = mo_kingdom("Bacteria"),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr      min       lq      mean    median       uq      max neval
-#     A 0.105181 0.121314 0.1478538 0.1465265 0.166711 0.211409    10
-#     B 0.132558 0.146388 0.1584278 0.1499835 0.164895 0.208477    10
-#     C 0.135492 0.160355 0.2341847 0.1884665 0.348857 0.395931    10
-#     D 0.109650 0.115727 0.1270481 0.1264130 0.128648 0.168317    10
-#     E 0.081574 0.096940 0.0992582 0.0980915 0.101479 0.120477    10
-#     F 0.081575 0.088489 0.0988463 0.0989650 0.103365 0.126482    10
-#     G 0.091981 0.095333 0.1043568 0.1001530 0.111327 0.129625    10
-#     H 0.092610 0.093169 0.1009135 0.0985455 0.101828 0.120406    10
-#     I 0.087371 0.091213 0.1069758 0.0941815 0.109302 0.192831    10
-```
-
-Of course, when running `mo_phylum("Firmicutes")` the function has zero knowledge about the actual microorganism, namely *S. aureus*. But since the result would be `"Firmicutes"` too, there is no point in calculating the result. And because this package 'knows' all phyla of all known microorganisms (according to ITIS), it can just return the initial value immediately.
-
-When the system language is non-English and supported by this `AMR` package, some functions take a little while longer:
-```r
-mo_fullname("CoNS", language = "en") # or just mo_fullname("CoNS") on an English system
-# "Coagulase Negative Staphylococcus (CoNS)"
-
-mo_fullname("CoNS", language = "fr") # or just mo_fullname("CoNS") on a French system
-# "Staphylococcus à coagulase négative (CoNS)"
-
-microbenchmark(en = mo_fullname("CoNS", language = "en"),
-               de = mo_fullname("CoNS", language = "de"),
-               nl = mo_fullname("CoNS", language = "nl"),
-               es = mo_fullname("CoNS", language = "es"),
-               it = mo_fullname("CoNS", language = "it"),
-               fr = mo_fullname("CoNS", language = "fr"),
-               pt = mo_fullname("CoNS", language = "pt"),
-               times = 10,
-               unit = "ms")
-# Unit: milliseconds
-#  expr       min       lq      mean    median        uq      max neval
-#    en  6.093583  6.51724  6.555105  6.562986  6.630663  6.99698   100
-#    de 13.934874 14.35137 16.891587 14.462210 14.764658 43.63956   100
-#    nl 13.900092 14.34729 15.943268 14.424565 14.581535 43.76283   100
-#    es 13.833813 14.34596 14.574783 14.439757 14.653994 17.49168   100
-#    it 13.811883 14.36621 15.179060 14.453515 14.812359 43.64284   100
-#    fr 13.798683 14.37019 16.344731 14.468775 14.697610 48.62923   100
-#    pt 13.789674 14.36244 15.706321 14.443772 14.679905 44.76701   100
-```
-
-Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
-
-## Copyright
-
-This R package is licensed under the [GNU General Public License (GPL) v2.0](https://gitlab.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
-
- May be used for commercial purposes
-
- May be used for private purposes
-
- May **not** be used for patent purposes
-
- May be modified, although:
-
-  - Modifications **must** be released under the same license when distributing the package
-  - Changes made to the code **must** be documented
-
- May be distributed, although:
-
-  - Source code **must** be made available when the package is distributed
-  - A copy of the license and copyright notice **must** be included with the package.
-
- Comes with a LIMITATION of liability
-
- Comes with NO warranty
@@ -1,58 +0,0 @@
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
+      <div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>The <code>AMR</code> package for R is a powerful tool for
+antimicrobial resistance (AMR) analysis. It provides extensive features
+for handling microbial and antimicrobial data. However, for those who
+work primarily in Python, we now have a more intuitive option available:
+the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
+package</a>.</p>
+<p>This Python package is a wrapper around the <code>AMR</code> R
+package. It uses the <code>rpy2</code> package internally. Despite the
+need to have R installed, Python users can now easily work with AMR data
+directly through Python code.</p>
+</div>
+<div class="section level2">
+<h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
+</h2>
+<p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
+(venv)</a>. You can set up such an environment by running:</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
+<p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
+the environment</a>, after which the venv is ready to work with.</p>
+</div>
+<div class="section level2">
+<h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
+</h2>
+<ol style="list-style-type: decimal">
+<li>
+<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
+just run:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
+</li>
+<li>
+<p>Make sure you have R installed. There is <strong>no need to
+install the <code>AMR</code> R package</strong>, as it will be installed
+automatically.</p>
+<p>For Linux:</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
+<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
+<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
+<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
+<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
+<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
+<p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
+<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
+download page</a> to download and install R.</p>
+</li>
+</ol>
+</div>
+<div class="section level2">
+<h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
+</h2>
+<div class="section level3">
+<h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
+</h3>
+<p>Here’s an example that demonstrates how to clean microorganism and
+drug names using the <code>AMR</code> Python package:</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
+<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
+<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
+<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>    <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
+<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>    <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
+<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
+<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
+<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
+<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
+<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
+<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
+<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
+<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
+<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th>MOs</th>
+<th>Drug</th>
+<th>MO_clean</th>
+<th>Drug_clean</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>E. coli</td>
+<td>Cipro</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="even">
+<td>ESCCOL</td>
+<td>CIP</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="odd">
+<td>esco</td>
+<td>J01MA02</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+<tr class="even">
+<td>Esche coli</td>
+<td>Ciproxin</td>
+<td>Escherichia coli</td>
+<td>Ciprofloxacin</td>
+</tr>
+</tbody>
+</table>
+<div class="section level4">
+<h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
+</h4>
+<ul>
+<li><p><strong>mo_name:</strong> This function standardises
+microorganism names. Here, different variations of <em>Escherichia
+coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
+all converted into the correct, standardised form, “Escherichia
+coli”.</p></li>
+<li><p><strong>ab_name</strong>: Similarly, this function standardises
+antimicrobial names. The different representations of ciprofloxacin
+(e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
+the standard name, “Ciprofloxacin”.</p></li>
+</ul>
+</div>
+</div>
+<div class="section level3">
+<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
+</h3>
+<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
+<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
+<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
+<pre><code>[0.59555556]</code></pre>
+</div>
+<div class="section level3">
+<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
+</h3>
+<p>One of the core functions of the <code>AMR</code> package is
+generating an antibiogram, a table that summarises the antimicrobial
+susceptibility of bacterial isolates. Here’s how you can generate an
+antibiogram from Python:</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
+<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="22%">
+<col width="22%">
+<col width="22%">
+<col width="33%">
+</colgroup>
+<thead><tr class="header">
+<th>Pathogen</th>
+<th>Amoxicillin</th>
+<th>Ciprofloxacin</th>
+<th>Piperacillin/tazobactam</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>CoNS</td>
+<td>7% (10/142)</td>
+<td>73% (183/252)</td>
+<td>30% (10/33)</td>
+</tr>
+<tr class="even">
+<td>E. coli</td>
+<td>50% (196/392)</td>
+<td>88% (399/456)</td>
+<td>94% (393/416)</td>
+</tr>
+<tr class="odd">
+<td>K. pneumoniae</td>
+<td>0% (0/58)</td>
+<td>96% (53/55)</td>
+<td>89% (47/53)</td>
+</tr>
+<tr class="even">
+<td>P. aeruginosa</td>
+<td>0% (0/30)</td>
+<td>100% (30/30)</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>P. mirabilis</td>
+<td>None</td>
+<td>94% (34/36)</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>S. aureus</td>
+<td>6% (8/131)</td>
+<td>90% (171/191)</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>S. epidermidis</td>
+<td>1% (1/91)</td>
+<td>64% (87/136)</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>S. hominis</td>
+<td>None</td>
+<td>80% (56/70)</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>S. pneumoniae</td>
+<td>100% (112/112)</td>
+<td>None</td>
+<td>100% (112/112)</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
+<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="20%">
+<col width="22%">
+<col width="23%">
+<col width="33%">
+</colgroup>
+<thead><tr class="header">
+<th>Pathogen</th>
+<th>Amoxicillin</th>
+<th>Ciprofloxacin</th>
+<th>Piperacillin/tazobactam</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>Gram-negative</td>
+<td>36% (226/631)</td>
+<td>91% (621/684)</td>
+<td>88% (565/641)</td>
+</tr>
+<tr class="even">
+<td>Gram-positive</td>
+<td>43% (305/703)</td>
+<td>77% (560/724)</td>
+<td>86% (296/345)</td>
+</tr>
+</tbody>
+</table>
+<p>In this example, we generate an antibiogram by selecting various
+antibiotics.</p>
+</div>
+<div class="section level3">
+<h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
+</h3>
+<p>As a Python user, you might like that the most important data sets of
+the <code>AMR</code> R package, <code>microorganisms</code>,
+<code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
+<code>example_isolates</code>, are now available as regular Python data
+frames:</p>
+<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="11%">
+<col width="29%">
+<col width="8%">
+<col width="8%">
+<col width="8%">
+<col width="10%">
+<col width="13%">
+<col width="9%">
+</colgroup>
+<thead><tr class="header">
+<th>mo</th>
+<th>fullname</th>
+<th>status</th>
+<th>kingdom</th>
+<th>gbif</th>
+<th>gbif_parent</th>
+<th>gbif_renamed_to</th>
+<th>prevalence</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>B_GRAMN</td>
+<td>(unknown Gram-negatives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_GRAMP</td>
+<td>(unknown Gram-positives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ANAER-NEG</td>
+<td>(unknown anaerobic Gram-negatives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ANAER-POS</td>
+<td>(unknown anaerobic Gram-positives)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ANAER</td>
+<td>(unknown anaerobic bacteria)</td>
+<td>unknown</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+</tr>
+<tr class="odd">
+<td>B_ZYMMN_POMC</td>
+<td>Zymomonas pomaceae</td>
+<td>accepted</td>
+<td>Bacteria</td>
+<td>10744418</td>
+<td>3221412</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ZYMPH</td>
+<td>Zymophilus</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>9475166</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>B_ZYMPH_PCVR</td>
+<td>Zymophilus paucivorans</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="even">
+<td>B_ZYMPH_RFFN</td>
+<td>Zymophilus raffinosivorans</td>
+<td>synonym</td>
+<td>Bacteria</td>
+<td>None</td>
+<td>None</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+<tr class="odd">
+<td>F_ZYZYG</td>
+<td>Zyzygomyces</td>
+<td>unknown</td>
+<td>Fungi</td>
+<td>None</td>
+<td>7581</td>
+<td>None</td>
+<td>2.0</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
+<table style="width:100%;" class="table">
+<colgroup>
+<col width="4%">
+<col width="12%">
+<col width="20%">
+<col width="25%">
+<col width="9%">
+<col width="11%">
+<col width="7%">
+<col width="9%">
+</colgroup>
+<thead><tr class="header">
+<th>ab</th>
+<th>cid</th>
+<th>name</th>
+<th>group</th>
+<th>oral_ddd</th>
+<th>oral_units</th>
+<th>iv_ddd</th>
+<th>iv_units</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>AMA</td>
+<td>4649.0</td>
+<td>4-aminosalicylic acid</td>
+<td>Antimycobacterials</td>
+<td>12.00</td>
+<td>g</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ACM</td>
+<td>6450012.0</td>
+<td>Acetylmidecamycin</td>
+<td>Macrolides/lincosamides</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>ASP</td>
+<td>49787020.0</td>
+<td>Acetylspiramycin</td>
+<td>Macrolides/lincosamides</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ALS</td>
+<td>8954.0</td>
+<td>Aldesulfone sodium</td>
+<td>Other antibacterials</td>
+<td>0.33</td>
+<td>g</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>AMK</td>
+<td>37768.0</td>
+<td>Amikacin</td>
+<td>Aminoglycosides</td>
+<td>NaN</td>
+<td>None</td>
+<td>1.0</td>
+<td>g</td>
+</tr>
+<tr class="even">
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+<td>…</td>
+</tr>
+<tr class="odd">
+<td>VIR</td>
+<td>11979535.0</td>
+<td>Virginiamycine</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>VOR</td>
+<td>71616.0</td>
+<td>Voriconazole</td>
+<td>Antifungals/antimycotics</td>
+<td>0.40</td>
+<td>g</td>
+<td>0.4</td>
+<td>g</td>
+</tr>
+<tr class="odd">
+<td>XBR</td>
+<td>72144.0</td>
+<td>Xibornol</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="even">
+<td>ZID</td>
+<td>77846445.0</td>
+<td>Zidebactam</td>
+<td>Other antibacterials</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+<tr class="odd">
+<td>ZFD</td>
+<td>NaN</td>
+<td>Zoliflodacin</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+<td>NaN</td>
+<td>None</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div class="section level2">
+<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
+</h2>
+<p>With the <code>AMR</code> Python package, Python users can now
+effortlessly call R functions from the <code>AMR</code> R package. This
+eliminates the need for complex <code>rpy2</code> configurations and
+provides a clean, easy-to-use interface for antimicrobial resistance
+analysis. The examples provided above demonstrate how this can be
+applied to typical workflows, such as standardising microorganism and
+antimicrobial names or calculating resistance.</p>
+<p>By just running <code>import AMR</code>, users can seamlessly
+integrate the robust features of the R <code>AMR</code> package into
+Python workflows.</p>
+<p>Whether you’re cleaning data or analysing resistance patterns, the
+<code>AMR</code> Python package makes it easy to work with AMR data in
+Python.</p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
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+<!DOCTYPE html>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>AMR with tidymodels</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_with_tidymodels.Rmd" class="external-link"><code>vignettes/AMR_with_tidymodels.Rmd</code></a></small>
+      <div class="d-none name"><code>AMR_with_tidymodels.Rmd</code></div>
+    </div>
+
+    
+    
+<blockquote>
+<p>This page was entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
+manually-trained model able to answer any question about the
+<code>AMR</code> package.</p>
+</blockquote>
+<p>Antimicrobial resistance (AMR) is a global health crisis, and
+understanding resistance patterns is crucial for managing effective
+treatments. The <code>AMR</code> R package provides robust tools for
+analysing AMR data, including convenient antimicrobial selector
+functions like <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and
+<code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>.</p>
+<p>In this post, we will explore how to use the <code>tidymodels</code>
+framework to predict resistance patterns in the
+<code>example_isolates</code> dataset in two examples.</p>
+<div class="section level2">
+<h2 id="example-1-using-antimicrobial-selectors">Example 1: Using Antimicrobial Selectors<a class="anchor" aria-label="anchor" href="#example-1-using-antimicrobial-selectors"></a>
+</h2>
+<p>By leveraging the power of <code>tidymodels</code> and the
+<code>AMR</code> package, we’ll build a reproducible machine learning
+workflow to predict the Gramstain of the microorganism to two important
+antibiotic classes: aminoglycosides and beta-lactams.</p>
+<div class="section level3">
+<h3 id="objective">
+<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective"></a>
+</h3>
+<p>Our goal is to build a predictive model using the
+<code>tidymodels</code> framework to determine the Gramstain of the
+microorganism based on microbial data. We will:</p>
+<ol style="list-style-type: decimal">
+<li>Preprocess data using the selector functions
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>.</li>
+<li>Define a logistic regression model for prediction.</li>
+<li>Use a structured <code>tidymodels</code> workflow to preprocess,
+train, and evaluate the model.</li>
+</ol>
+</div>
+<div class="section level3">
+<h3 id="data-preparation">
+<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation"></a>
+</h3>
+<p>We begin by loading the required libraries and preparing the
+<code>example_isolates</code> dataset from the <code>AMR</code>
+package.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Load required libraries</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span>          <span class="co"># For AMR data analysis</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span>   <span class="co"># For machine learning workflows, and data manipulation (dplyr, tidyr, ...)</span></span></code></pre></div>
+<p>Prepare the data:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Your data could look like this:</span></span>
+<span><span class="va">example_isolates</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
+<span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
+<span></span>
+<span><span class="co"># Select relevant columns for prediction</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># select AB results dynamically</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">mo</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># replace NAs with NI (not-interpretable)</span></span>
+<span>   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>                 <span class="op">~</span><span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span><span class="op">)</span>,</span>
+<span>          <span class="co"># make factors of SIR columns</span></span>
+<span>          <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
+<span>                 <span class="va">as.integer</span><span class="op">)</span>,</span>
+<span>          <span class="co"># get Gramstain of microorganisms</span></span>
+<span>          mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># drop NAs - the ones without a Gramstain (fungi, etc.)</span></span>
+<span>  <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>
+dynamically select columns for antimicrobials in these classes.</li>
+<li>
+<code>drop_na()</code> ensures the model receives complete cases for
+training.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="defining-the-workflow">
+<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow"></a>
+</h3>
+<p>We now define the <code>tidymodels</code> workflow, which consists of
+three steps: preprocessing, model specification, and fitting.</p>
+<div class="section level4">
+<h4 id="preprocessing-with-a-recipe">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe"></a>
+</h4>
+<p>We create a recipe to preprocess the data for modelling.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Define the recipe for data preprocessing</span></span>
+<span><span class="va">resistance_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">mo</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">step_corr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, threshold <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
+<span><span class="va">resistance_recipe</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; outcome:    1</span></span>
+<span><span class="co">#&gt; predictor: 20</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">c(aminoglycosides(), betalactams())</span></span></span></code></pre></div>
+<p>For a recipe that includes at least one preprocessing operation, like
+we have with <code>step_corr()</code>, the necessary parameters can be
+estimated from a training set using <code>prep()</code>:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">prep</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; outcome:    1</span></span>
+<span><span class="co">#&gt; predictor: 20</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Training information</span></span>
+<span><span class="co">#&gt; Training data contained 1968 data points and no incomplete rows.</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">AMX</span> <span style="color: #0000BB;">CTX</span> | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>recipe(mo ~ ., data = data)</code> will take the
+<code>mo</code> column as outcome and all other columns as
+predictors.</li>
+<li>
+<code>step_corr()</code> removes predictors (i.e., antibiotic
+columns) that have a higher correlation than 90%.</li>
+</ul>
+<p>Notice how the recipe contains just the antimicrobial selector
+functions - no need to define the columns specifically. In the
+preparation (retrieved with <code>prep()</code>) we can see that the
+columns or variables ‘AMX’ and ‘CTX’ were removed as they correlate too
+much with existing, other variables.</p>
+</div>
+<div class="section level4">
+<h4 id="specifying-the-model">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model"></a>
+</h4>
+<p>We define a logistic regression model since resistance prediction is
+a binary classification task.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Specify a logistic regression model</span></span>
+<span><span class="va">logistic_model</span> <span class="op">&lt;-</span> <span class="fu">logistic_reg</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">set_engine</span><span class="op">(</span><span class="st">"glm"</span><span class="op">)</span> <span class="co"># Use the Generalised Linear Model engine</span></span>
+<span><span class="va">logistic_model</span></span>
+<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>logistic_reg()</code> sets up a logistic regression
+model.</li>
+<li>
+<code>set_engine("glm")</code> specifies the use of R’s built-in GLM
+engine.</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="building-the-workflow">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow"></a>
+</h4>
+<p>We bundle the recipe and model together into a <code>workflow</code>,
+which organises the entire modelling process.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Combine the recipe and model into a workflow</span></span>
+<span><span class="va">resistance_workflow</span> <span class="op">&lt;-</span> <span class="fu">workflow</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Add the preprocessing recipe</span></span>
+<span>  <span class="fu">add_model</span><span class="op">(</span><span class="va">logistic_model</span><span class="op">)</span> <span class="co"># Add the logistic regression model</span></span>
+<span><span class="va">resistance_workflow</span></span>
+<span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; 1 Recipe Step</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; • step_corr()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
+</div>
+</div>
+<div class="section level3">
+<h3 id="training-and-evaluating-the-model">
+<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model"></a>
+</h3>
+<p>To train the model, we split the data into training and testing sets.
+Then, we fit the workflow on the training set and evaluate its
+performance.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Split data into training and testing sets</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span> <span class="co"># For reproducibility</span></span>
+<span><span class="va">data_split</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span> <span class="co"># 80% training, 20% testing</span></span>
+<span><span class="va">training_data</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span> <span class="co"># Training set</span></span>
+<span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span>   <span class="co"># Testing set</span></span>
+<span></span>
+<span><span class="co"># Fit the workflow to the training data</span></span>
+<span><span class="va">fitted_workflow</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">fit</span><span class="op">(</span><span class="va">training_data</span><span class="op">)</span> <span class="co"># Train the model</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>initial_split()</code> splits the data into training and
+testing sets.</li>
+<li>
+<code>fit()</code> trains the workflow on the training set.</li>
+</ul>
+<p>Notice how in <code>fit()</code>, the antimicrobial selector
+functions are internally called again. For training, these functions are
+called since they are stored in the recipe.</p>
+<p>Next, we evaluate the model on the testing data.</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Make predictions on the testing set</span></span>
+<span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span>                <span class="co"># Generate predictions</span></span>
+<span><span class="va">probabilities</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</span><span class="op">)</span> <span class="co"># Generate probabilities</span></span>
+<span></span>
+<span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">probabilities</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span> <span class="co"># Combine with true labels</span></span>
+<span></span>
+<span><span class="va">predictions</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 394 × 24</span></span></span>
+<span><span class="co">#&gt;    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB</span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> Gram-positive              3.17<span style="color: #949494;">e</span><span style="color: #BB0000;">- 8</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     5     1</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> Gram-negative              9.99<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              1.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 3</span> Gram-n…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> Gram-negative              9.46<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              5.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 2</span> Gram-n…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     1     5</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Gram-negative              1   <span style="color: #949494;">e</span>+ 0              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span> Gram-n…     1     1</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> Gram-positive              6.05<span style="color: #949494;">e</span><span style="color: #BB0000;">-11</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 384 more rows</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 18 more variables: AMK &lt;int&gt;, KAN &lt;int&gt;, PEN &lt;int&gt;, OXA &lt;int&gt;, FLC &lt;int&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   AMX &lt;int&gt;, AMC &lt;int&gt;, AMP &lt;int&gt;, TZP &lt;int&gt;, CZO &lt;int&gt;, FEP &lt;int&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;int&gt;, FOX &lt;int&gt;, CTX &lt;int&gt;, CAZ &lt;int&gt;, CRO &lt;int&gt;, IPM &lt;int&gt;, MEM &lt;int&gt;</span></span></span>
+<span></span>
+<span><span class="co"># Evaluate model performance</span></span>
+<span><span class="va">metrics</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">mo</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span> <span class="co"># Calculate performance metrics</span></span>
+<span></span>
+<span><span class="va">metrics</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 3</span></span></span>
+<span><span class="co">#&gt;   .metric  .estimator .estimate</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.995</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.989</span></span>
+<span></span>
+<span></span>
+<span><span class="co"># To assess some other model properties, you can make our own `metrics()` function</span></span>
+<span><span class="va">our_metrics</span> <span class="op">&lt;-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">kap</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span> <span class="co"># add Positive Predictive Value and Negative Predictive Value</span></span>
+<span><span class="va">metrics2</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">our_metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">mo</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span> <span class="co"># run again on our `our_metrics()` function</span></span>
+<span></span>
+<span><span class="va">metrics2</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 3</span></span></span>
+<span><span class="co">#&gt;   .metric  .estimator .estimate</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.995</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.989</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> ppv      binary         0.987</span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> npv      binary         1</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> generates predictions on the testing
+set.</li>
+<li>
+<code>metrics()</code> computes evaluation metrics like accuracy and
+kappa.</li>
+</ul>
+<p>It appears we can predict the Gram stain with a 99.5% accuracy based
+on AMR results of only aminoglycosides and beta-lactam antibiotics. The
+ROC curve looks like this:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" width="720"></p>
+</div>
+<div class="section level3">
+<h3 id="conclusion">
+<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
+</h3>
+<p>In this post, we demonstrated how to build a machine learning
+pipeline with the <code>tidymodels</code> framework and the
+<code>AMR</code> package. By combining selector functions like
+<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
+<code>tidymodels</code>, we efficiently prepared data, trained a model,
+and evaluated its performance.</p>
+<p>This workflow is extensible to other antimicrobial classes and
+resistance patterns, empowering users to analyse AMR data systematically
+and reproducibly.</p>
+<hr>
+</div>
+</div>
+<div class="section level2">
+<h2 id="example-2-predicting-amr-over-time">Example 2: Predicting AMR Over Time<a class="anchor" aria-label="anchor" href="#example-2-predicting-amr-over-time"></a>
+</h2>
+<p>In this second example, we aim to predict antimicrobial resistance
+(AMR) trends over time using <code>tidymodels</code>. We will model
+resistance to three antibiotics (amoxicillin <code>AMX</code>,
+amoxicillin-clavulanic acid <code>AMC</code>, and ciprofloxacin
+<code>CIP</code>), based on historical data grouped by year and hospital
+ward.</p>
+<div class="section level3">
+<h3 id="objective-1">
+<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
+</h3>
+<p>Our goal is to:</p>
+<ol style="list-style-type: decimal">
+<li>Prepare the dataset by aggregating resistance data over time.</li>
+<li>Define a regression model to predict AMR trends.</li>
+<li>Use <code>tidymodels</code> to preprocess, train, and evaluate the
+model.</li>
+</ol>
+</div>
+<div class="section level3">
+<h3 id="data-preparation-1">
+<strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation-1"></a>
+</h3>
+<p>We start by transforming the <code>example_isolates</code> dataset
+into a structured time-series format.</p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Load required libraries</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Transform dataset</span></span>
+<span><span class="va">data_time</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Filter on the top #10 species</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>year <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/format.html" class="external-link">format</a></span><span class="op">(</span><span class="va">date</span>, <span class="st">"%Y"</span><span class="op">)</span><span class="op">)</span>,  <span class="co"># Extract year from date</span></span>
+<span>         gramstain <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Get taxonomic names</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">year</span>, <span class="va">gramstain</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">AMX</span>, <span class="va">AMC</span>, <span class="va">CIP</span><span class="op">)</span>, </span>
+<span>                   <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">x</span>, minimum <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>                   .names <span class="op">=</span> <span class="st">"res_{.col}"</span><span class="op">)</span>, </span>
+<span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMX</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMC</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_CIP</span><span class="op">)</span><span class="op">)</span> <span class="co"># Drop missing values</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">mo</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">col_mo</span><span style="color: #0000BB;">.</span></span></span>
+<span></span>
+<span><span class="va">data_time</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 32 × 5</span></span></span>
+<span><span class="co">#&gt;     year gramstain     res_AMX res_AMC res_CIP</span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>  <span style="text-decoration: underline;">2</span>002 Gram-negative   1      0.105   0.060<span style="text-decoration: underline;">6</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>  <span style="text-decoration: underline;">2</span>002 Gram-positive   0.838  0.182   0.162 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>  <span style="text-decoration: underline;">2</span>003 Gram-negative   1      0.071<span style="text-decoration: underline;">4</span>  0     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>  <span style="text-decoration: underline;">2</span>003 Gram-positive   0.714  0.244   0.154 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>  <span style="text-decoration: underline;">2</span>004 Gram-negative   0.464  0.093<span style="text-decoration: underline;">8</span>  0     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>  <span style="text-decoration: underline;">2</span>004 Gram-positive   0.849  0.299   0.244 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>  <span style="text-decoration: underline;">2</span>005 Gram-negative   0.412  0.132   0.058<span style="text-decoration: underline;">8</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>  <span style="text-decoration: underline;">2</span>005 Gram-positive   0.882  0.382   0.154 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>  <span style="text-decoration: underline;">2</span>006 Gram-negative   0.379  0       0.1   </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>  <span style="text-decoration: underline;">2</span>006 Gram-positive   0.778  0.333   0.353 </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 22 more rows</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>mo_name(mo)</code>: Converts microbial codes into proper
+species names.</li>
+<li>
+<code><a href="../reference/proportion.html">resistance()</a></code>: Converts AMR results into numeric values
+(proportion of resistant isolates).</li>
+<li>
+<code>group_by(year, ward, species)</code>: Aggregates resistance
+rates by year and ward.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="defining-the-workflow-1">
+<strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow-1"></a>
+</h3>
+<p>We now define the modelling workflow, which consists of a
+preprocessing step, a model specification, and the fitting process.</p>
+<div class="section level4">
+<h4 id="preprocessing-with-a-recipe-1">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-1"></a>
+</h4>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Define the recipe</span></span>
+<span><span class="va">resistance_recipe_time</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">res_AMX</span> <span class="op">~</span> <span class="va">year</span> <span class="op">+</span> <span class="va">gramstain</span>, data <span class="op">=</span> <span class="va">data_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Convert categorical to numerical</span></span>
+<span>  <span class="fu">step_normalize</span><span class="op">(</span><span class="va">year</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Normalise year for better model performance</span></span>
+<span>  <span class="fu">step_nzv</span><span class="op">(</span><span class="fu">all_predictors</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>  <span class="co"># Remove near-zero variance predictors</span></span>
+<span></span>
+<span><span class="va">resistance_recipe_time</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Inputs</span></span>
+<span><span class="co">#&gt; Number of variables by role</span></span>
+<span><span class="co">#&gt; outcome:   1</span></span>
+<span><span class="co">#&gt; predictor: 2</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Operations</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Dummy variables from: <span style="color: #0000BB;">gramstain</span></span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Centering and scaling for: <span style="color: #0000BB;">year</span></span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Sparse, unbalanced variable filter on: <span style="color: #0000BB;">all_predictors()</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>step_dummy()</code>: Encodes categorical variables
+(<code>ward</code>, <code>species</code>) as numerical indicators.</li>
+<li>
+<code>step_normalize()</code>: Normalises the <code>year</code>
+variable.</li>
+<li>
+<code>step_nzv()</code>: Removes near-zero variance predictors.</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="specifying-the-model-1">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-1"></a>
+</h4>
+<p>We use a linear regression model to predict resistance trends.</p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Define the linear regression model</span></span>
+<span><span class="va">lm_model</span> <span class="op">&lt;-</span> <span class="fu">linear_reg</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">set_engine</span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span> <span class="co"># Use linear regression</span></span>
+<span></span>
+<span><span class="va">lm_model</span></span>
+<span><span class="co">#&gt; Linear Regression Model Specification (regression)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: lm</span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>linear_reg()</code>: Defines a linear regression model.</li>
+<li>
+<code>set_engine("lm")</code>: Uses R’s built-in linear regression
+engine.</li>
+</ul>
+</div>
+<div class="section level4">
+<h4 id="building-the-workflow-1">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-1"></a>
+</h4>
+<p>We combine the preprocessing recipe and model into a workflow.</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
+<span><span class="va">resistance_workflow_time</span> <span class="op">&lt;-</span> <span class="fu">workflow</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">add_model</span><span class="op">(</span><span class="va">lm_model</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">resistance_workflow_time</span></span>
+<span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
+<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> linear_reg()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; 3 Recipe Steps</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; • step_dummy()</span></span>
+<span><span class="co">#&gt; • step_normalize()</span></span>
+<span><span class="co">#&gt; • step_nzv()</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
+<span><span class="co">#&gt; Linear Regression Model Specification (regression)</span></span>
+<span><span class="co">#&gt; </span></span>
+<span><span class="co">#&gt; Computational engine: lm</span></span></code></pre></div>
+</div>
+</div>
+<div class="section level3">
+<h3 id="training-and-evaluating-the-model-1">
+<strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model-1"></a>
+</h3>
+<p>We split the data into training and testing sets, fit the model, and
+evaluate performance.</p>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Split the data</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
+<span><span class="va">data_split_time</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data_time</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
+<span><span class="va">train_time</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split_time</span><span class="op">)</span></span>
+<span><span class="va">test_time</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split_time</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Train the model</span></span>
+<span><span class="va">fitted_workflow_time</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">fit</span><span class="op">(</span><span class="va">train_time</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Make predictions</span></span>
+<span><span class="va">predictions_time</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> </span>
+<span></span>
+<span><span class="co"># Evaluate model</span></span>
+<span><span class="va">metrics_time</span> <span class="op">&lt;-</span> <span class="va">predictions_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu">metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">res_AMX</span>, estimate <span class="op">=</span> <span class="va">.pred</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">metrics_time</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3 × 3</span></span></span>
+<span><span class="co">#&gt;   .metric .estimator .estimate</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> rmse    standard      0.077<span style="text-decoration: underline;">4</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> rsq     standard      0.711 </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> mae     standard      0.070<span style="text-decoration: underline;">4</span></span></span></code></pre></div>
+<p><strong>Explanation:</strong></p>
+<ul>
+<li>
+<code>initial_split()</code>: Splits data into training and testing
+sets.</li>
+<li>
+<code>fit()</code>: Trains the workflow.</li>
+<li>
+<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code>: Generates resistance predictions.</li>
+<li>
+<code>metrics()</code>: Evaluates model performance.</li>
+</ul>
+</div>
+<div class="section level3">
+<h3 id="visualising-predictions">
+<strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions"></a>
+</h3>
+<p>We plot resistance trends over time for amoxicillin.</p>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Plot actual vs predicted resistance over time</span></span>
+<span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">predictions_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="st">"Actual"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">.pred</span>, color <span class="op">=</span> <span class="st">"Predicted"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Predicted vs Actual AMX Resistance Over Time"</span>,</span>
+<span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
+<span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" width="720"></p>
+<p>Additionally, we can visualise resistance trends in
+<code>ggplot2</code> and directly add linear models there:</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span>, y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="va">gramstain</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"AMX Resistance Trends"</span>,</span>
+<span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
+<span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="co"># add a linear model directly in ggplot2:</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_smooth.html" class="external-link">geom_smooth</a></span><span class="op">(</span>method <span class="op">=</span> <span class="st">"lm"</span>,</span>
+<span>              formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
+<span>              alpha <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" width="720"></p>
+</div>
+<div class="section level3">
+<h3 id="conclusion-1">
+<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion-1"></a>
+</h3>
+<p>In this example, we demonstrated how to analyze AMR trends over time
+using <code>tidymodels</code>. By aggregating resistance rates by year
+and hospital ward, we built a predictive model to track changes in
+resistance to amoxicillin (<code>AMX</code>), amoxicillin-clavulanic
+acid (<code>AMC</code>), and ciprofloxacin (<code>CIP</code>).</p>
+<p>This method can be extended to other antibiotics and resistance
+patterns, providing valuable insights into AMR dynamics in healthcare
+settings.</p>
+</div>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
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+    <li><a class="dropdown-item" href="../reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
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+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
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+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Apply EUCAST rules</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
+      <div class="d-none name"><code>EUCAST.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>What are EUCAST rules? The European Committee on Antimicrobial
+Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
+their website</a>:</p>
+<blockquote>
+<p><em>EUCAST expert rules (see below) are a tabulated collection of
+expert knowledge on interpretive rules, expected resistant phenotypes
+and expected susceptible phenotypes which should be applied to
+antimicrobial susceptibility testing in order to reduce testing, reduce
+errors and make appropriate recommendations for reporting particular
+resistances.</em></p>
+</blockquote>
+<p>In Europe, a lot of medical microbiological laboratories already
+apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
+<em>et al.</em>, 2015</a>). Our package features their latest insights
+on expected resistant phenotypes (v1.2, 2023).</p>
+</div>
+<div class="section level2">
+<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
+</h2>
+<p>These rules can be used to discard improbable bug-drug combinations
+in your data. For example, <em>Klebsiella</em> produces beta-lactamase
+that prevents ampicillin (or amoxicillin) from working against it. In
+other words, practically every strain of <em>Klebsiella</em> is
+resistant to ampicillin.</p>
+<p>Sometimes, laboratory data can still contain such strains with
+<em>Klebsiella</em> being susceptible to ampicillin. This could be
+because an antibiogram is available before an identification is
+available, and the antibiogram is then not re-interpreted based on the
+identification. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function resolves this,
+by applying the latest ‘EUCAST Expected Resistant Phenotypes’
+guideline:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
+<span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>    <span class="st">"Escherichia coli"</span></span>
+<span>  <span class="op">)</span>,</span>
+<span>  ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="va">oops</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
+<span><span class="co">#&gt;   mo                    ampicillin</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;">  S  </span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
+<span><span class="co">#&gt;   mo                    ampicillin</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FFAFAF;">  R  </span>     </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span></code></pre></div>
+<p>A more convenient function is
+<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
+but allows to check for one or more specific microorganisms or
+antimicrobials:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
+<span>  <span class="st">"ampicillin"</span></span>
+<span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1]  TRUE FALSE</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
+<span>  <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
+<span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1]  TRUE FALSE</span></span></code></pre></div>
+<p>EUCAST rules can not only be used for correction, they can also be
+used for filling in known resistance and susceptibility based on results
+of other antimicrobials drugs. This process is called <em>interpretive
+reading</em>, and is basically a form of imputation:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
+<span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
+<span>    <span class="st">"Staphylococcus aureus"</span>,</span>
+<span>    <span class="st">"Enterococcus faecalis"</span>,</span>
+<span>    <span class="st">"Escherichia coli"</span>,</span>
+<span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
+<span>    <span class="st">"Pseudomonas aeruginosa"</span></span>
+<span>  <span class="op">)</span>,</span>
+<span>  VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
+<span>  AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
+<span>  COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
+<span>  CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
+<span>  CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
+<span>  PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
+<span>  FOX <span class="op">=</span> <span class="st">"S"</span>  <span class="co"># Cefoxitin</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">mo</th>
+<th align="center">VAN</th>
+<th align="center">AMX</th>
+<th align="center">COL</th>
+<th align="center">CAZ</th>
+<th align="center">CXM</th>
+<th align="center">PEN</th>
+<th align="center">FOX</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Staphylococcus aureus</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Enterococcus faecalis</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Escherichia coli</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Klebsiella pneumoniae</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Pseudomonas aeruginosa</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">mo</th>
+<th align="center">VAN</th>
+<th align="center">AMX</th>
+<th align="center">COL</th>
+<th align="center">CAZ</th>
+<th align="center">CXM</th>
+<th align="center">PEN</th>
+<th align="center">FOX</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Staphylococcus aureus</td>
+<td align="center">-</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Enterococcus faecalis</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">R</td>
+</tr>
+<tr class="odd">
+<td align="left">Escherichia coli</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">S</td>
+</tr>
+<tr class="even">
+<td align="left">Klebsiella pneumoniae</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">S</td>
+</tr>
+<tr class="odd">
+<td align="left">Pseudomonas aeruginosa</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">-</td>
+<td align="center">-</td>
+<td align="center">R</td>
+<td align="center">R</td>
+<td align="center">R</td>
+</tr>
+</tbody>
+</table>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
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+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
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+<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="../reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Conduct principal component analysis (PCA) for AMR</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
+      <div class="d-none name"><code>PCA.Rmd</code></div>
+    </div>
+
+    
+    
+<p><strong>NOTE: This page will be updated soon, as the pca() function
+is currently being developed.</strong></p>
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+</div>
+<div class="section level2">
+<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
+</h2>
+<p>For PCA, we need to transform our AMR data first. This is what the
+<code>example_isolates</code> data set in this package looks like:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Rows: 2,000</span></span>
+<span><span class="co">#&gt; Columns: 46</span></span>
+<span><span class="co">#&gt; $ date    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
+<span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
+<span><span class="co">#&gt; $ age     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
+<span><span class="co">#&gt; $ gender  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
+<span><span class="co">#&gt; $ ward    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
+<span><span class="co">#&gt; $ mo      <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ PEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ OXA     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ FLC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
+<span><span class="co">#&gt; $ AMX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ AMC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ AMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ TZP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CZO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ FEP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CXM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ FOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ CTX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ CAZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ CRO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ GEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ TOB     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ AMK     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ KAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ TMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ SXT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ NIT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ FOS     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ LNZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ CIP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ MFX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ VAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ TEC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
+<span><span class="co">#&gt; $ TCY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ TGC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ DOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ ERY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ CLI     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ AZM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
+<span><span class="co">#&gt; $ IPM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
+<span><span class="co">#&gt; $ MEM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ MTR     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ CHL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ COL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
+<span><span class="co">#&gt; $ MUP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
+<span><span class="co">#&gt; $ RIF     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
+<p>Now to transform this to a data set with only resistance percentages
+per taxonomic order and genus:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
+<span>    order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
+<span>    genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
+<span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co">#  and genus as we do here</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
+<span>    <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
+<span>    <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
+<span>  <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># Groups:   order [5]</span></span></span>
+<span><span class="co">#&gt;   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT</span></span>
+<span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (unknown order)   (unknown ge…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Actinomycetales   Schaalia        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Bacteroidales     Bacteroides     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Caryophanales     Gemella         <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> Caryophanales     Listeria        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
+</h2>
+<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
+that contain numeric values in all selected variables, so we now only
+need to do:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
+<span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span></span>
+<span><span class="co"><span style="color: #0000BB;">#&gt;   "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></span></span></code></pre></div>
+<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
+function:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
+<span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span>
+<span><span class="co">#&gt; Importance of components:</span></span>
+<span><span class="co">#&gt;                           PC1    PC2    PC3     PC4     PC5     PC6       PC7</span></span>
+<span><span class="co">#&gt; Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
+<span><span class="co">#&gt; Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
+<span><span class="co">#&gt; Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
+<pre><code><span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
+<span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span></code></pre>
+<p>Good news. The first two components explain a total of 93.3% of the
+variance (see the PC1 and PC2 values of the <em>Proportion of
+Variance</em>. We can create a so-called biplot with the base R
+<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
+per drug explain the difference per microorganism.</p>
+</div>
+<div class="section level2">
+<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
+</h2>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
+<p>But we can’t see the explanation of the points. Perhaps this works
+better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
+automatically adds the right labels and even groups:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
+<p>You can also print an ellipse per group, and edit the appearance:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
+<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
+</div>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Work with WHONET data</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
+      <div class="d-none name"><code>WHONET.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level3">
+<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
+</h3>
+<p>This tutorial assumes you already imported the WHONET data with
+e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
+package</a>. In RStudio, this can be done using the menu button ‘Import
+Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
+select your exported file. Make sure date fields are imported
+correctly.</p>
+<p>An example syntax could look like this:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></span></code></pre></div>
+<p>This package comes with an <a href="https://amr-for-r.org/reference/WHONET.html">example data set
+<code>WHONET</code></a>. We will use it for this analysis.</p>
+</div>
+<div class="section level3">
+<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
+</h3>
+<p>First, load the relevant packages if you did not yet did this. I use
+the tidyverse for all of my analyses. All of them. If you don’t know it
+yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># this package</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
+<p>We will have to transform some variables to simplify and automate the
+analysis:</p>
+<ul>
+<li>Microorganisms should be transformed to our own microorganism codes
+(called an <code>mo</code>) using <a href="https://amr-for-r.org/reference/catalogue_of_life">our Catalogue
+of Life reference data set</a>, which contains all ~70,000
+microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
+We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
+recognises almost all WHONET abbreviations of microorganisms.</li>
+<li>Antimicrobial results or interpretations have to be clean and valid.
+In other words, they should only contain values <code>"S"</code>,
+<code>"I"</code> or <code>"R"</code>. That is exactly where the
+<code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
+</ul>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># transform variables</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># get microbial ID based on given organism</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
+<p>No errors or warnings, so all values are transformed succesfully.</p>
+<p>We also created a package dedicated to data cleaning and checking,
+called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
+function can be used to create frequency tables.</p>
+<p>So let’s check our data, with a couple of frequency tables:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
+<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: character<br>
+Length: 500<br>
+Available: 500 (100%, NA: 0 = 0%)<br>
+Unique: 38</p>
+<p>Shortest: 11<br>
+Longest: 40</p>
+<table class="table">
+<colgroup>
+<col width="4%">
+<col width="47%">
+<col width="7%">
+<col width="10%">
+<col width="13%">
+<col width="15%">
+</colgroup>
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Item</th>
+<th align="right">Count</th>
+<th align="right">Percent</th>
+<th align="right">Cum. Count</th>
+<th align="right">Cum. Percent</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">1</td>
+<td align="left">Escherichia coli</td>
+<td align="right">245</td>
+<td align="right">49.0%</td>
+<td align="right">245</td>
+<td align="right">49.0%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
+<td align="right">74</td>
+<td align="right">14.8%</td>
+<td align="right">319</td>
+<td align="right">63.8%</td>
+</tr>
+<tr class="odd">
+<td align="left">3</td>
+<td align="left">Staphylococcus epidermidis</td>
+<td align="right">38</td>
+<td align="right">7.6%</td>
+<td align="right">357</td>
+<td align="right">71.4%</td>
+</tr>
+<tr class="even">
+<td align="left">4</td>
+<td align="left">Streptococcus pneumoniae</td>
+<td align="right">31</td>
+<td align="right">6.2%</td>
+<td align="right">388</td>
+<td align="right">77.6%</td>
+</tr>
+<tr class="odd">
+<td align="left">5</td>
+<td align="left">Staphylococcus hominis</td>
+<td align="right">21</td>
+<td align="right">4.2%</td>
+<td align="right">409</td>
+<td align="right">81.8%</td>
+</tr>
+<tr class="even">
+<td align="left">6</td>
+<td align="left">Proteus mirabilis</td>
+<td align="right">9</td>
+<td align="right">1.8%</td>
+<td align="right">418</td>
+<td align="right">83.6%</td>
+</tr>
+<tr class="odd">
+<td align="left">7</td>
+<td align="left">Enterococcus faecium</td>
+<td align="right">8</td>
+<td align="right">1.6%</td>
+<td align="right">426</td>
+<td align="right">85.2%</td>
+</tr>
+<tr class="even">
+<td align="left">8</td>
+<td align="left">Staphylococcus capitis urealyticus</td>
+<td align="right">8</td>
+<td align="right">1.6%</td>
+<td align="right">434</td>
+<td align="right">86.8%</td>
+</tr>
+<tr class="odd">
+<td align="left">9</td>
+<td align="left">Enterobacter cloacae</td>
+<td align="right">5</td>
+<td align="right">1.0%</td>
+<td align="right">439</td>
+<td align="right">87.8%</td>
+</tr>
+<tr class="even">
+<td align="left">10</td>
+<td align="left">Enterococcus columbae</td>
+<td align="right">4</td>
+<td align="right">0.8%</td>
+<td align="right">443</td>
+<td align="right">88.6%</td>
+</tr>
+</tbody>
+</table>
+<p>(omitted 28 entries, n = 57 [11.4%])</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
+<span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
+<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
+<p><strong>Frequency table</strong></p>
+<p>Class: factor &gt; ordered &gt; sir (numeric)<br>
+Length: 500<br>
+Levels: 5: S &lt; SDD &lt; I &lt; R &lt; NI<br>
+Available: 481 (96.2%, NA: 19 = 3.8%)<br>
+Unique: 3</p>
+<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
+Drug group: Beta-lactams/penicillins<br>
+%SI: 78.59%</p>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Item</th>
+<th align="right">Count</th>
+<th align="right">Percent</th>
+<th align="right">Cum. Count</th>
+<th align="right">Cum. Percent</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">1</td>
+<td align="left">S</td>
+<td align="right">356</td>
+<td align="right">74.01%</td>
+<td align="right">356</td>
+<td align="right">74.01%</td>
+</tr>
+<tr class="even">
+<td align="left">2</td>
+<td align="left">R</td>
+<td align="right">103</td>
+<td align="right">21.41%</td>
+<td align="right">459</td>
+<td align="right">95.43%</td>
+</tr>
+<tr class="odd">
+<td align="left">3</td>
+<td align="left">I</td>
+<td align="right">22</td>
+<td align="right">4.57%</td>
+<td align="right">481</td>
+<td align="right">100.00%</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
+</h3>
+<p>An easy <code>ggplot</code> will already give a lot of information,
+using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
+<span>  <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
+      <div class="d-none name"><code>WISCA.Rmd</code></div>
+    </div>
+
+    
+    
+<blockquote>
+<p>This explainer was largely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
+manually-trained model able to answer any question about the
+<code>AMR</code> package.</p>
+</blockquote>
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>Clinical guidelines for empirical antimicrobial therapy require
+<em>probabilistic reasoning</em>: what is the chance that a regimen will
+cover the likely infecting organisms, before culture results are
+available?</p>
+<p>This is the purpose of <strong>WISCA</strong>, or
+<strong>Weighted-Incidence Syndromic Combination
+Antibiogram</strong>.</p>
+<p>WISCA is a Bayesian approach that integrates:</p>
+<ul>
+<li>
+<strong>Pathogen prevalence</strong> (how often each species causes
+the syndrome),</li>
+<li>
+<strong>Regimen susceptibility</strong> (how often a regimen works
+<em>if</em> the pathogen is known),</li>
+</ul>
+<p>to estimate the <strong>overall empirical coverage</strong> of
+antimicrobial regimens, with quantified uncertainty.</p>
+<p>This vignette explains how WISCA works, why it is useful, and how to
+apply it using the <code>AMR</code> package.</p>
+</div>
+<div class="section level2">
+<h2 id="why-traditional-antibiograms-fall-short">Why traditional antibiograms fall short<a class="anchor" aria-label="anchor" href="#why-traditional-antibiograms-fall-short"></a>
+</h2>
+<p>A standard antibiogram gives you:</p>
+<pre><code>Species → Antibiotic → Susceptibility %</code></pre>
+<p>But clinicians don’t know the species <em>a priori</em>. They need to
+choose a regimen that covers the <strong>likely pathogens</strong>,
+without knowing which one is present.</p>
+<p>Traditional antibiograms calculate the susceptibility % as just the
+number of resistant isolates divided by the total number of tested
+isolates. Therefore, traditional antibiograms:</p>
+<ul>
+<li>Fragment information by organism,</li>
+<li>Do not weight by real-world prevalence,</li>
+<li>Do not account for combination therapy or sample size,</li>
+<li>Do not provide uncertainty.</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="the-idea-of-wisca">The idea of WISCA<a class="anchor" aria-label="anchor" href="#the-idea-of-wisca"></a>
+</h2>
+<p>WISCA asks:</p>
+<blockquote>
+<p>“What is the <strong>probability</strong> that this regimen
+<strong>will cover</strong> the pathogen, given the syndrome?”</p>
+</blockquote>
+<p>This means combining two things:</p>
+<ul>
+<li>
+<strong>Incidence</strong> of each pathogen in the syndrome,</li>
+<li>
+<strong>Susceptibility</strong> of each pathogen to the
+regimen.</li>
+</ul>
+<p>We can write this as:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mrow><mo stretchy="true" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
+<p>For example, suppose:</p>
+<ul>
+<li>
+<em>E. coli</em> causes 60% of cases, and 90% of <em>E. coli</em>
+are susceptible to a drug.</li>
+<li>
+<em>Klebsiella</em> causes 40% of cases, and 70% of
+<em>Klebsiella</em> are susceptible.</li>
+</ul>
+<p>Then:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
+<p>But in real data, incidence and susceptibility are <strong>estimated
+from samples</strong>, so they carry uncertainty. WISCA models this
+<strong>probabilistically</strong>, using conjugate Bayesian
+distributions.</p>
+</div>
+<div class="section level2">
+<h2 id="the-bayesian-engine-behind-wisca">The Bayesian engine behind WISCA<a class="anchor" aria-label="anchor" href="#the-bayesian-engine-behind-wisca"></a>
+</h2>
+<div class="section level3">
+<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
+</h3>
+<p>Let:</p>
+<ul>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
+be the number of pathogens,</li>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math>
+be a <strong>Dirichlet</strong> prior (uniform),</li>
+<li>
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">n = (n_1, \ldots, n_K)</annotation></semantics></math>
+be the observed counts per species.</li>
+</ul>
+<p>Then the posterior incidence is:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
+<p>To simulate from this, we use:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
+</div>
+<div class="section level3">
+<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
+</h3>
+<p>Each pathogen–regimen pair has a prior and data:</p>
+<ul>
+<li>Prior:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>,
+with default
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math>
+</li>
+<li>Data:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
+susceptible out of
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
+tested</li>
+</ul>
+<p>The
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
+category could also include values SDD (susceptible, dose-dependent) and
+I (intermediate [CLSI], or susceptible, increased exposure
+[EUCAST]).</p>
+<p>Then the posterior is:</p>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
+</div>
+<div class="section level3">
+<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
+</h3>
+<p>Putting it together:</p>
+<ol style="list-style-type: decimal">
+<li>Simulate pathogen incidence:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
+</li>
+<li>Simulate susceptibility:
+<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math>
+</li>
+<li>Combine:</li>
+</ol>
+<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
+<p>Repeat this simulation (e.g. 1000×) and summarise:</p>
+<ul>
+<li>
+<strong>Mean</strong> = expected coverage</li>
+<li>
+<strong>Quantiles</strong> = credible interval</li>
+</ul>
+</div>
+</div>
+<div class="section level2">
+<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
+</h2>
+<div class="section level3">
+<h3 id="prepare-data-and-simulate-synthetic-syndrome">Prepare data and simulate synthetic syndrome<a class="anchor" aria-label="anchor" href="#prepare-data-and-simulate-synthetic-syndrome"></a>
+</h3>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
+<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span></span>
+<span></span>
+<span><span class="co"># Structure of our data</span></span>
+<span><span class="va">data</span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
+<span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
+<span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
+<span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
+<span></span>
+<span><span class="co"># Add a fake syndrome column</span></span>
+<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"No UTI"</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="basic-wisca-antibiogram">Basic WISCA antibiogram<a class="anchor" aria-label="anchor" href="#basic-wisca-antibiogram"></a>
+</h3>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Ciprofloxacin</th>
+<th align="left">Gentamicin</th>
+</tr></thead>
+<tbody><tr class="odd">
+<td align="left">73.7% (71.7-75.8%)</td>
+<td align="left">77% (74.3-79.4%)</td>
+<td align="left">72.8% (70.7-74.8%)</td>
+</tr></tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
+</h3>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="24%">
+<col width="38%">
+<col width="36%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
+<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
+</tr></thead>
+<tbody><tr class="odd">
+<td align="left">73.8% (71.8-75.7%)</td>
+<td align="left">87.5% (85.9-89%)</td>
+<td align="left">89.7% (88.2-91.1%)</td>
+</tr></tbody>
+</table>
+</div>
+<div class="section level3">
+<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
+</h3>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>      syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="12%">
+<col width="21%">
+<col width="34%">
+<col width="31%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Syndromic Group</th>
+<th align="left">Amoxicillin/clavulanic acid</th>
+<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
+<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">No UTI</td>
+<td align="left">70.1% (67.8-72.3%)</td>
+<td align="left">85.2% (83.1-87.2%)</td>
+<td align="left">87.1% (85.3-88.7%)</td>
+</tr>
+<tr class="even">
+<td align="left">UTI</td>
+<td align="left">80.9% (77.7-83.8%)</td>
+<td align="left">88.2% (85.7-90.5%)</td>
+<td align="left">90.9% (88.7-93%)</td>
+</tr>
+</tbody>
+</table>
+<p>The <code>AMR</code> package is available in 28 languages, which can
+all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
+<span>      antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
+<span>      syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
+<span>      language <span class="op">=</span> <span class="st">"Spanish"</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="12%">
+<col width="21%">
+<col width="34%">
+<col width="31%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Grupo sindrómico</th>
+<th align="left">Amoxicilina/ácido clavulánico</th>
+<th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
+<th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">No UCI</td>
+<td align="left">70% (67.8-72.4%)</td>
+<td align="left">85.3% (83.3-87.2%)</td>
+<td align="left">87% (85.3-88.8%)</td>
+</tr>
+<tr class="even">
+<td align="left">UCI</td>
+<td align="left">80.9% (77.7-83.9%)</td>
+<td align="left">88.2% (85.5-90.6%)</td>
+<td align="left">90.9% (88.7-93%)</td>
+</tr>
+</tbody>
+</table>
+</div>
+</div>
+<div class="section level2">
+<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
+</h2>
+<ul>
+<li>
+<code>simulations = 1000</code>: number of Monte Carlo draws</li>
+<li>
+<code>conf_interval = 0.95</code>: coverage interval width</li>
+<li>
+<code>combine_SI = TRUE</code>: count “I” and “SDD” as
+susceptible</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
+</h2>
+<ul>
+<li>It assumes your data are representative</li>
+<li>No adjustment for patient-level covariates, although these could be
+passed onto the <code>syndromic_group</code> argument</li>
+<li>WISCA does not model resistance over time, you might want to use
+<code>tidymodels</code> for that, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
+basic introduction</a>
+</li>
+</ul>
+</div>
+<div class="section level2">
+<h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
+</h2>
+<p>WISCA enables:</p>
+<ul>
+<li>Empirical regimen comparison,</li>
+<li>Syndrome-specific coverage estimation,</li>
+<li>Fully probabilistic interpretation.</li>
+</ul>
+<p>It is available in the <code>AMR</code> package via either:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="reference">Reference<a class="anchor" aria-label="anchor" href="#reference"></a>
+</h2>
+<p>Bielicki, JA, et al. (2016). <em>Selecting appropriate empirical
+antibiotic regimens for paediatric bloodstream infections: application
+of a Bayesian decision model to local and pooled antimicrobial
+resistance surveillance data.</em> <strong>J Antimicrob
+Chemother</strong>. 71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
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+
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+</html>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9288</small>
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="../articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="../articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="../reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="../articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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+      <h3>All vignettes</h3>
+      <div class="section-desc"></div>
+
+      <dl><dt><a href="AMR_for_Python.html">AMR for Python</a></dt>
+        <dd>
+        </dd><dt><a href="AMR_with_tidymodels.html">AMR with tidymodels</a></dt>
+        <dd>
+        </dd><dt><a href="AMR.html">Conduct AMR data analysis</a></dt>
+        <dd>
+        </dd><dt><a href="datasets.html">Download data sets for download / own use</a></dt>
+        <dd>
+        </dd><dt><a href="EUCAST.html">Apply EUCAST rules</a></dt>
+        <dd>
+        </dd><dt><a href="PCA.html">Conduct principal component analysis (PCA) for AMR</a></dt>
+        <dd>
+        </dd><dt><a href="WHONET.html">Work with WHONET data</a></dt>
+        <dd>
+        </dd><dt><a href="WISCA.html">Estimating Empirical Coverage with WISCA</a></dt>
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+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
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+
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9288</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
+    <li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
+    <li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
+    <li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
+    <li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
+    <li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
+    <li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
+    <li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
+    <li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
+    <li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
+    <li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
+    <li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
+<li class="active nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
+<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
+      </ul></div>
+
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+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="logo.svg" class="logo" alt=""><h1>Authors and Citation</h1>
+    </div>
+
+    <div class="section level2">
+      <h2>Authors</h2>
+
+      <ul class="list-unstyled"><li>
+          <p><strong>Matthijs S. Berends</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7620-1800" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Dennis Souverein</strong>. Author, contributor. <a href="https://orcid.org/0000-0003-0455-0336" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Erwin E. A. Hassing</strong>. Author, contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Aislinn Cook</strong>. Contributor. <a href="https://orcid.org/0000-0002-9189-7815" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Andrew P. Norgan</strong>. Contributor. <a href="https://orcid.org/0000-0002-2955-2066" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anita Williams</strong>. Contributor. <a href="https://orcid.org/0000-0002-5295-8451" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Annick Lenglet</strong>. Contributor. <a href="https://orcid.org/0000-0003-2013-8405" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anthony Underwood</strong>. Contributor. <a href="https://orcid.org/0000-0002-8547-4277" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Anton Mymrikov</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Bart C. Meijer</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Christian F. Luz</strong>. Contributor. <a href="https://orcid.org/0000-0001-5809-5995" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Dmytro Mykhailenko</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Gwen Knight</strong>. Contributor. <a href="https://orcid.org/0000-0002-7263-9896" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jason Stull</strong>. Contributor. <a href="https://orcid.org/0000-0002-9028-8153" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Javier Sanchez</strong>. Contributor. <a href="https://orcid.org/0000-0003-2605-8094" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jonas Salm</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Judith M. Fonville</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Matthew Saab</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Rogier P. Schade</strong>. Contributor.
+          </p>
+        </li>
+        <li>
+          <p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+      </ul></div>
+
+    <div class="section level2">
+      <h2 id="citation">Citation</h2>
+      <p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
+
+      <p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
+“AMR: An R Package for Working with Antimicrobial Resistance Data.”
+<em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
+<a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
+</p>
+      <pre>@Article{,
+  title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
+  author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
+  journal = {Journal of Statistical Software},
+  year = {2022},
+  volume = {104},
+  number = {3},
+  pages = {1--31},
+  doi = {10.18637/jss.v104.i03},
+}</pre>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
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--- a/Show More
+++ b/Show More