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634 changed files with 54810 additions and 3677 deletions
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@@ -0,0 +1 @@
+amr-for-r.org
@@ -1,32 +0,0 @@
-Package: AMR
-Version: 0.1.1
-Date: 2018-02-22
-Title: Antimicrobial Resistance Analysis
-Authors@R: c(
-    person(
-        given = c("Matthijs", "S."),
-        family = "Berends",
-        email = "m.s.berends@umcg.nl",
-        role = c("aut", "cre")),
-    person(
-        given = c("Christian", "F."),
-        family = "Luz",
-        email = "c.f.luz@umcg.nl",
-        role = c("aut", "ctb")),
-    person(
-        given = c("Erwin", "E.A."),
-        family = "Hassing",
-        email = "e.hassing@certe.nl",
-        role = "ctb"))
-Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
-    of microbial isolates, by using new S3 classes and applying EUCAST expert rules
-    on antibiograms according to Leclercq (2013)
-    <doi:10.1111/j.1469-0691.2011.03703.x>.
-Depends: R (>= 3.0)
-Imports: dplyr (>= 0.7.0), reshape2 (>= 1.4.0), xml2, rvest
-URL: https://github.com/msberends/AMR
-BugReports: https://github.com/msberends/AMR/issues
-License: GPL-2 | file LICENSE
-Encoding: UTF-8
-LazyData: true
-RoxygenNote: 6.0.1.9000
@@ -1,339 +0,0 @@
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-free software which everyone can redistribute and change under these terms.
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-  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 @@
+<!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>License • 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="License"><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="">2.1.1.9281</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="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="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>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<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
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+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>
+
@@ -1,68 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method(as.double,mic)
-S3method(as.integer,mic)
-S3method(as.numeric,mic)
-S3method(plot,mic)
-S3method(plot,rsi)
-S3method(print,mic)
-S3method(print,rsi)
-S3method(summary,mic)
-S3method(summary,rsi)
-export(EUCAST_rules)
-export(abname)
-export(anti_join_bactlist)
-export(as.mic)
-export(as.rsi)
-export(atc_property)
-export(full_join_bactlist)
-export(inner_join_bactlist)
-export(interpretive_reading)
-export(is.mic)
-export(is.rsi)
-export(key_antibiotics)
-export(left_join_bactlist)
-export(mo_property)
-export(right_join_bactlist)
-export(rsi)
-export(rsi_df)
-export(rsi_predict)
-export(semi_join_bactlist)
-exportMethods(as.double.mic)
-exportMethods(as.integer.mic)
-exportMethods(as.numeric.mic)
-exportMethods(plot.mic)
-exportMethods(plot.rsi)
-exportMethods(print.mic)
-exportMethods(print.rsi)
-exportMethods(summary.mic)
-exportMethods(summary.rsi)
-importFrom(dplyr,"%>%")
-importFrom(dplyr,all_vars)
-importFrom(dplyr,any_vars)
-importFrom(dplyr,arrange)
-importFrom(dplyr,arrange_at)
-importFrom(dplyr,between)
-importFrom(dplyr,filter)
-importFrom(dplyr,filter_at)
-importFrom(dplyr,group_by)
-importFrom(dplyr,group_by_at)
-importFrom(dplyr,if_else)
-importFrom(dplyr,lag)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
-importFrom(dplyr,n_distinct)
-importFrom(dplyr,progress_estimated)
-importFrom(dplyr,pull)
-importFrom(dplyr,row_number)
-importFrom(dplyr,select)
-importFrom(dplyr,slice)
-importFrom(dplyr,summarise)
-importFrom(dplyr,tibble)
-importFrom(dplyr,vars)
-importFrom(graphics,plot)
-importFrom(graphics,text)
-importFrom(reshape2,dcast)
-importFrom(rvest,html_nodes)
-importFrom(rvest,html_table)
-importFrom(xml2,read_html)
@@ -1,638 +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.                         #
-# ==================================================================== #
-
-#' EUCAST expert rules
-#'
-#' Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
-#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
-#' @param col_bactcode column name of the bacteria ID in \code{tbl} - should also be present in \code{bactlist$bactid}, see \code{\link{bactlist}}.
-#' @param info print progress
-#' @param 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,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.
-#' @param ... parameters that are passed on to \code{EUCAST_rules}
-#' @name EUCAST
-#' @rdname EUCAST
-#' @export
-#' @importFrom dplyr %>% left_join select
-#' @return table with edited variables of antibiotics.
-#' @source
-#'   EUCAST Expert Rules Version 2.0: \cr
-#'   Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
-#'   \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
-#'   \cr
-#'   EUCAST Expert Rules Version 3.1: \cr
-#'   \url{http://www.eucast.org/expert_rules_and_intrinsic_resistance}
-#' @examples
-#' \dontrun{
-#' tbl <- EUCAST_rules(tbl)
-#' }
-EUCAST_rules <- function(tbl,
-                         col_bactcode = 'bacteriecode',
-                         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',
-                         mino = 'mino',
-                         moxi = 'moxi',
-                         nali = 'nali',
-                         neom = 'neom',
-                         neti = 'neti',
-                         nitr = 'nitr',
-                         novo = 'novo',
-                         norf = 'norf',
-                         oflo = 'oflo',
-                         peni = 'peni',
-                         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') {
-  
-  if (!col_bactcode %in% colnames(tbl)) {
-    stop('Column ', col_bactcode, ' not found.')
-  }
-  
-  # kolommen controleren
-  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep,
-                cfot, cfox, cfta, cftr, cfur, cipr, clar, clin, clox, coli, czol,
-                dapt, doxy, erta, eryt, fusi, gent, imip, kana, levo, linc, line,
-                mero, 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 <- col.list[!is.na(col.list)]
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      cat('\n')
-    }
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored:\n',
-              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-    if (!amcl %in% colnames(tbl)) { amcl <- NA }
-    if (!amik %in% colnames(tbl)) { amik <- NA }
-    if (!amox %in% colnames(tbl)) { amox <- NA }
-    if (!ampi %in% colnames(tbl)) { ampi <- NA }
-    if (!azit %in% colnames(tbl)) { azit <- NA }
-    if (!aztr %in% colnames(tbl)) { aztr <- NA }
-    if (!cefa %in% colnames(tbl)) { cefa <- NA }
-    if (!cfra %in% colnames(tbl)) { cfra <- NA }
-    if (!cfep %in% colnames(tbl)) { cfep <- NA }
-    if (!cfot %in% colnames(tbl)) { cfot <- NA }
-    if (!cfox %in% colnames(tbl)) { cfox <- NA }
-    if (!cfta %in% colnames(tbl)) { cfta <- NA }
-    if (!cftr %in% colnames(tbl)) { cftr <- NA }
-    if (!cfur %in% colnames(tbl)) { cfur <- NA }
-    if (!chlo %in% colnames(tbl)) { chlo <- NA }
-    if (!cipr %in% colnames(tbl)) { cipr <- NA }
-    if (!clar %in% colnames(tbl)) { clar <- NA }
-    if (!clin %in% colnames(tbl)) { clin <- NA }
-    if (!clox %in% colnames(tbl)) { clox <- NA }
-    if (!coli %in% colnames(tbl)) { coli <- NA }
-    if (!czol %in% colnames(tbl)) { czol <- NA }
-    if (!dapt %in% colnames(tbl)) { dapt <- NA }
-    if (!doxy %in% colnames(tbl)) { doxy <- NA }
-    if (!erta %in% colnames(tbl)) { erta <- NA }
-    if (!eryt %in% colnames(tbl)) { eryt <- NA }
-    if (!fosf %in% colnames(tbl)) { fosf <- NA }
-    if (!fusi %in% colnames(tbl)) { fusi <- NA }
-    if (!gent %in% colnames(tbl)) { gent <- NA }
-    if (!imip %in% colnames(tbl)) { imip <- NA }
-    if (!kana %in% colnames(tbl)) { kana <- NA }
-    if (!levo %in% colnames(tbl)) { levo <- NA }
-    if (!linc %in% colnames(tbl)) { linc <- NA }
-    if (!line %in% colnames(tbl)) { line <- NA }
-    if (!mero %in% colnames(tbl)) { mero <- NA }
-    if (!mino %in% colnames(tbl)) { mino <- NA }
-    if (!moxi %in% colnames(tbl)) { moxi <- NA }
-    if (!nali %in% colnames(tbl)) { nali <- NA }
-    if (!neom %in% colnames(tbl)) { neom <- NA }
-    if (!neti %in% colnames(tbl)) { neti <- NA }
-    if (!nitr %in% colnames(tbl)) { nitr <- NA }
-    if (!novo %in% colnames(tbl)) { novo <- NA }
-    if (!norf %in% colnames(tbl)) { norf <- NA }
-    if (!oflo %in% colnames(tbl)) { oflo <- NA }
-    if (!peni %in% colnames(tbl)) { peni <- NA }
-    if (!pita %in% colnames(tbl)) { pita <- NA }
-    if (!poly %in% colnames(tbl)) { poly <- NA }
-    if (!qida %in% colnames(tbl)) { qida <- NA }
-    if (!rifa %in% colnames(tbl)) { rifa <- NA }
-    if (!roxi %in% colnames(tbl)) { roxi <- NA }
-    if (!siso %in% colnames(tbl)) { siso <- NA }
-    if (!teic %in% colnames(tbl)) { teic <- NA }
-    if (!tetr %in% colnames(tbl)) { tetr <- NA }
-    if (!tica %in% colnames(tbl)) { tica <- NA }
-    if (!tige %in% colnames(tbl)) { tige <- NA }
-    if (!tobr %in% colnames(tbl)) { tobr <- NA }
-    if (!trim %in% colnames(tbl)) { trim <- NA }
-    if (!trsu %in% colnames(tbl)) { trsu <- NA }
-    if (!vanc %in% colnames(tbl)) { vanc <- NA }
-  }
-  
-  total <- 0
-  
-  # functie voor uitvoeren
-  edit_rsi <- function(to, rows, cols) {
-    #voortgang$tick()$print()
-    cols <- cols[!is.na(cols)]
-    if (length(rows) > 0 & length(cols) > 0) {
-      tbl[rows, cols] <<- to
-      total <<- total + (length(rows) * length(cols))
-    }
-  }
-  
-  # bactlist aan vastknopen (bestaande kolommen krijgen extra suffix)
-  joinby <- colnames(AMR::bactlist)[1]
-  names(joinby) <- col_bactcode
-  tbl <- tbl %>% left_join(y = AMR::bactlist, by = joinby, suffix = c("_tempbactlist", ""))
-  
-  # antibioticagroepen
-  aminoglycosiden <- c(tobr, gent, kana, neom, neti, siso)
-  tetracyclines <- c(doxy, mino, tetr) # sinds EUCAST v3.1 is tige(cycline) apart
-  polymyxines <- c(poly, coli)
-  macroliden <- c(eryt, azit, roxi, clar) # sinds EUCAST v3.1 is clinda apart
-  glycopeptiden <- c(vanc, teic)
-  streptogramines <- qida # eigenlijk pristinamycine en quinupristine/dalfopristine
-  cefalosporines <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
-  carbapenems <- c(erta, imip, mero)
-  aminopenicillines <- c(ampi, amox)
-  ureidopenicillines <- pita # eigenlijk ook azlo en mezlo
-  fluorochinolonen <- c(oflo, cipr, norf, levo, moxi)
-  
-  if (info == TRUE) {
-    cat('\nApplying EUCAST expert rules on',
-        tbl[!is.na(tbl$genus),] %>% nrow(),
-        'isolates according to "EUCAST Expert Rules Version 3.1"\n\n')
-  }
-  
-  # Table 1: Intrinsic resistance in Enterobacteriaceae ----
-  if (info == TRUE) {
-    cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
-  }
-  #voortgang <- progress_estimated(17)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$family == 'Enterobacteriaceae'),
-           cols = c(peni, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
-  # Citrobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)'),
-           cols = c(ampi, tica))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)'),
-           cols = c(ampi, amcl, czol, cfox))
-  # Enterobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter cloacae'),
-           cols = c(ampi, amcl, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterobacter aerogenes'),
-           cols = c(ampi, amcl, czol, cfox))
-  # Escherichia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Escherichia hermanni'),
-           cols = c(ampi, tica))
-  # Hafnia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Hafnia alvei'),
-           cols = c(ampi, amcl, czol, cfox))
-  # Klebsiella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Klebsiella'),
-           cols = c(ampi, tica))
-  # Morganella / Proteus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Morganella morganii'),
-           cols = c(ampi, amcl, czol, tetracyclines, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus mirabilis'),
-           cols = c(tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus penneri'),
-           cols = c(ampi, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Proteus vulgaris'),
-           cols = c(ampi, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  # Providencia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia rettgeri'),
-           cols = c(ampi, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Providencia stuartii'),
-           cols = c(ampi, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
-  # Raoultella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Raoultella'),
-           cols = c(ampi, tica))
-  # Serratia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Serratia marcescens'),
-           cols = c(ampi, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != 'mino'], polymyxines, nitr))
-  # Yersinia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia enterocolitica'),
-           cols = c(ampi, amcl, tica, czol, cfox))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
-           cols = c(poly, coli))
-  
-  
-  # Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(8)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Achromobacter',
-                                         'Acinetobacter',
-                                         'Alcaligenes',
-                                         'Bordatella',
-                                         'Burkholderia',
-                                         'Elizabethkingia',
-                                         'Flavobacterium',
-                                         'Ochrobactrum',
-                                         'Pseudomonas',
-                                         'Stenotrophomonas')),
-           cols = c(peni, cfox, cfur, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
-  # Acinetobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Acinetobacter (baumannii|pittii|nosocomialis|calcoaceticus)'),
-           cols = c(ampi, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, tetracyclines[tetracyclines != 'mino']))
-  # Achromobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Achromobacter (xylosoxydans|xylosoxidans)'),
-           cols = c(ampi, czol, cfot, cftr, erta))
-  # Burkholderia
-  edit_rsi(to = 'R',
-           # onder 'Burkholderia cepacia complex' vallen deze species allemaal: PMID 16217180.
-           rows = which(tbl$fullname %like% '^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis)'),
-           cols = c(ampi, amcl, tica, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosiden, trim, fosf, polymyxines))
-  # Elizabethkingia
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Elizabethkingia meningoseptic(a|um)'),
-           cols = c(ampi, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxines))
-  # Ochrobactrum
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Ochrobactrum anthropi'),
-           cols = c(ampi, amcl, tica, pita, czol, cfot, cftr, cfta, cfep, aztr, erta))
-  # Pseudomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Pseudomonas aeruginosa'),
-           cols = c(ampi, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige))
-  # Stenotrophomonas
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
-           cols = c(ampi, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosiden, trim, fosf, tetr))
-  
-  
-  # Table 3: Intrinsic resistance in other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(7)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Haemophilus',
-                                         'Moraxella',
-                                         'Neisseria',
-                                         'Campylobacter')),
-           cols = c(glycopeptiden, linc, dapt, line))
-  # Haemophilus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Haemophilus influenzae'),
-           cols = c(fusi, streptogramines))
-  # Moraxella
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Moraxella catarrhalis'),
-           cols = trim)
-  # Neisseria
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Neisseria'),
-           cols = trim)
-  # Campylobacter
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter fetus'),
-           cols = c(fusi, streptogramines, trim, nali))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
-           cols = c(fusi, streptogramines, trim))
-  
-  
-  # Table 4: Intrinsic resistance in Gram-positive bacteria ----
-  if (info == TRUE) {
-    cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
-  }
-  #voortgang <- progress_estimated(14)
-  # Intrisiek R voor groep
-  edit_rsi(to = 'R',
-           rows = which(tbl$gramstain %like% 'Positi(e|)(v|f)'),
-           cols = c(aztr, polymyxines, nali))
-  # Staphylococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus saprophyticus'),
-           cols = c(fusi, cfta, fosf, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (cohnii|xylosus)'),
-           cols = c(cfta, novo))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus capitis'),
-           cols = c(cfta, fosf))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
-           cols = cfta)
-  # Streptococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Streptococcus'),
-           cols = c(fusi, cfta, aminoglycosiden))
-  # Enterococcus
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecalis'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus (gallinarum|casseliflavus)'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, vanc, trim, trsu))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Enterococcus faecium'),
-           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, trim, trsu))
-  # Corynebacterium
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Corynebacterium'),
-           cols = fosf)
-  # Listeria
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Listeria monocytogenes'),
-           cols = c(cfta, cefalosporines[cefalosporines != cfta]))
-  # overig
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$genus == 'Lactobacillus'),
-           cols = c(vanc, teic))
-  edit_rsi(to = 'R',
-           rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
-           cols = vanc)
-  
-  # Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
-  if (info == TRUE) {
-    cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
-  }
-  #voortgang <- progress_estimated(2)
-  # regel 8.3
-  if (!is.na(peni)) {
-    edit_rsi(to = 'S',
-             rows = which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|dysgalactiae|groep A|groep B|groep C|groep G)'
-                          & tbl[, peni] == 'S'),
-             cols = c(aminopenicillines, cefalosporines, carbapenems))
-  }
-  # regel 8.6
-  if (!is.na(ampi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Enterococcus'
-                          & tbl[, ampi] == 'R'),
-             cols = c(ureidopenicillines, carbapenems))
-  }
-  
-  # Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
-  if (info == TRUE) {
-    cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
-  }
-  #voortgang <- progress_estimated(1)
-  # regel 9.3
-  if (!is.na(tica) & !is.na(pita)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tica] == 'R'
-                          & tbl[, pita] == 'S'),
-             cols = pita)
-  }
-  
-  # Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
-  if (info == TRUE) {
-    cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
-  }
-  #voortgang <- progress_estimated(1)
-  # regel 10.2
-  if (!is.na(ampi)) {
-    # hiervoor moeten we eerst weten of ze B-lactamase-positief zijn
-    # edit_rsi(to = 'R',
-    #          rows = which(tbl$fullname %like% '^Haemophilus influenza'
-    #                       & tbl[, ampi] == 'R'),
-    #          cols = c(ampi, amox, amcl, pita, cfur))
-  }
-  
-  # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
-  if (info == TRUE) {
-    cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
-  }
-  # regel 11.1
-  if (!is.na(eryt)) {
-    if (!is.na(azit)) {
-      tbl[, azit] <- tbl[, eryt]
-    }
-    if (!is.na(clar)) {
-      tbl[, clar] <- tbl[, eryt]
-    }
-  }
-  
-  # Table 12: Interpretive rules for aminoglycosides ----
-  if (info == TRUE) {
-    cat('...Table 12: Interpretive rules for aminoglycosides\n')
-  }
-  #voortgang <- progress_estimated(4)
-  # regel 12.2
-  if (!is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, tobr] == 'R'),
-             cols = c(kana, amik))
-  }
-  # regel 12.3
-  if (!is.na(gent)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, gent] == 'R'),
-             cols = aminoglycosiden)
-  }
-  # regel 12.8
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, gent] == 'I'
-                          & tbl[, tobr] == 'S'),
-             cols = gent)
-  }
-  # regel 12.9
-  if (!is.na(gent) & !is.na(tobr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, tobr] == 'I'
-                          & tbl[, gent] == 'R'),
-             cols = tobr)
-  }
-  
-  
-  # Table 13: Interpretive rules for quinolones ----
-  if (info == TRUE) {
-    cat('...Table 13: Interpretive rules for quinolones\n')
-  }
-  #voortgang <- progress_estimated(4)
-  # regel 13.2
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$genus == 'Staphylococcus'
-                          & tbl[, moxi] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.4
-  if (!is.na(moxi)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Streptococcus pneumoniae'
-                          & tbl[, moxi] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.5
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$family == 'Enterobacteriaceae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluorochinolonen)
-  }
-  # regel 13.8
-  if (!is.na(cipr)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl$fullname %like% '^Neisseria gonorrhoeae'
-                          & tbl[, cipr] == 'R'),
-             cols = fluorochinolonen)
-  }
-  
-  
-  # Other ----
-  if (info == TRUE) {
-    cat('...Other\n')
-  }
-  #voortgang <- progress_estimated(2)
-  if (!is.na(amcl)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, amcl] == 'R'),
-             cols = ampi)
-  }
-  if (!is.na(trsu)) {
-    edit_rsi(to = 'R',
-             rows = which(tbl[, trsu] == 'R'),
-             cols = trim)
-  }
-  if (!is.na(ampi) & !is.na(amox)) {
-    tbl[, amox] <- tbl[, ampi]
-  }
-  
-  # Toegevoegde kolommen weer verwijderen
-  bactlist.ncol <- ncol(AMR::bactlist) - 2
-  tbl.ncol <- ncol(tbl)
-  tbl <- tbl %>% select(-c((tbl.ncol - bactlist.ncol):tbl.ncol))
-  # en eventueel toegevoegde suffix aan bestaande kolommen weer verwijderen
-  colnames(tbl) <- gsub("_tempbactlist", "", colnames(tbl))
-  
-  if (info == TRUE) {
-    cat('\nDone.\nExpert rules applied to', total, 'test results.\n')
-  }
-  
-  tbl
-}
-
-#' @rdname EUCAST
-#' @export
-interpretive_reading <- function(...) {
-  EUCAST_rules(...)
-}
-
-#' Poperties of a microorganism
-#'
-#' @param bactcode ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}
-#' @param property One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}
-#' @export
-#' @importFrom dplyr %>% filter select
-#' @seealso \code{\link{bactlist}}
-mo_property <- function(bactcode, property = 'fullname') {
-  
-  mocode <- as.character(bactcode)
-  
-  for (i in 1:length(mocode)) {
-    bug <- mocode[i]
-    
-    if (!is.na(bug)) {
-      result = tryCatch({
-        mocode[i] <-
-          AMR::bactlist %>%
-          filter(bactid == bactcode) %>%
-          select(property) %>%
-          unlist() %>%
-          as.character()
-      }, error = function(error_condition) {
-        warning('Code ', bug, ' not found in bacteria list.')
-      }, finally = {
-        if (mocode[i] == bug & !property %in% c('bactid', 'bactsys')) {
-          mocode[i] <- NA
-        }
-      })
-    }
-    
-  }
-  mocode
-}
@@ -1,226 +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.                         #
-# ==================================================================== #
-
-#' Properties of an ATC code
-#'
-#' Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic. \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"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
-#' @param administration type of administration, see \emph{Details}
-#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
-#' @details
-#' Abbreviations for the property \code{"Adm.R"} (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 for the property \code{"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
-#' @importFrom dplyr %>% progress_estimated
-#' @importFrom xml2 read_html
-#' @importFrom rvest html_nodes html_table
-#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
-#' @examples 
-#' \donttest{
-#' atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
-#' atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
-#' }
-atc_property <- function(atc_code,
-                         property,
-                         administration = 'O',
-                         url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
-  
-  # property <- property %>% tolower()
-  #
-  if (property %like% 'unit') {
-    property <- 'U'
-  }
-  
-  # validation of properties
-  valid_properties.bak <- c("ATC code", "Name", "DDD", "U", "Adm.R", "Note")
-  valid_properties <- valid_properties.bak #%>% tolower()
-  if (!property %in% valid_properties) {
-    stop('Invalid `property`, use one of ', paste(valid_properties, collapse = ", "), '.')
-  }
-  
-  returnvalue <- rep(NA_character_, length(atc_code))
-  if (property == 'DDD') {
-    returnvalue <- rep(NA_real_, 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)
-    tbl <- xml2::read_html(atc_url) %>%
-      rvest::html_nodes('table') %>%
-      rvest::html_table(header = TRUE)
-    
-    if (length(tbl) == 0) {
-      warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
-      returnvalue[i] <- NA
-      next
-    }
-    
-    tbl <- tbl[[1]]
-    
-    if (property == 'Name') {
-      returnvalue[i] <- tbl[1, 2]
-    } else {
-      
-      names(returnvalue)[i] <- tbl[1, 2] %>% as.character()
-      
-      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]
-          }
-        }
-      }
-    }
-  }
-  
-  cat('\n')
-  returnvalue
-  
-}
-
-#' Name of an antibiotic
-#'
-#' Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{ablist}}.
-#' @param abcode a code or name, like \code{"amox"}, \code{"cftr"} or \code{"J01CA04"}
-#' @param from,to type to transform from and to. See \code{\link{ablist}} for its column names.
-#' @param textbetween text to put between multiple returned texts
-#' @param tolower return output as lower case with function \code{\link{tolower}}.
-#' @keywords ab antibiotics
-#' @source \code{\link{ablist}}
-#' @export
-#' @importFrom dplyr %>% filter select slice 
-#' @examples
-#' abname("AMCL")
-#' # "amoxicillin and enzyme inhibitor"
-#'
-#' abname("AMCL+GENT")
-#' # "amoxicillin and enzyme inhibitor + gentamicin"
-#'
-#' abname(c("AMCL", "GENT"))
-#' # "amoxicillin and enzyme inhibitor" "gentamicin" 
-#'
-#' abname("AMCL", to = "trivial")
-#' # "Amoxicilline/clavulaanzuur"
-#'
-#' abname("AMCL", to = "atc")
-#' # "J01CR02"
-#'
-#' abname("J01CR02", from = "atc", to = "umcg")
-#' # "AMCL"
-abname <- function(abcode, from = 'umcg', to = 'official', textbetween = ' + ', tolower = FALSE) {
-  
-  ablist <- AMR::ablist
-  colnames(ablist) <- colnames(ablist) %>% tolower()
-  from <- from %>% tolower()
-  to <- to %>% tolower()
-  
-  if (!from %in% colnames(ablist) |
-      !to %in% colnames(ablist)) {
-    stop(paste0('Invalid `from` or `to`. Choose one of ', 
-                colnames(ablist) %>% paste(collapse = ","), '.'), call. = FALSE)
-  }
-  
-  abcode <- as.character(abcode)
-  
-  for (i in 1:length(abcode)) {
-    drug <- abcode[i]
-    if (!grepl('+', drug, fixed = TRUE) & !grepl(' en ', drug, fixed = TRUE)) {
-      # bestaat maar uit 1 middel
-      if (any(ablist[, from] == drug)) {
-        abcode[i] <-
-          ablist %>%
-          filter(.[, from] == drug) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-      } else {
-        # niet gevonden
-        warning('Code "', drug, '" not found in antibiotics list.', call. = FALSE)
-        abcode[i] <- NA
-      }
-    } else {
-      # meerdere middelen
-      if (grepl('+', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, '+', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else if (grepl(' en ', drug, fixed = TRUE)) {
-        drug.group <-
-          strsplit(drug, ' en ', fixed = TRUE) %>%
-          unlist() %>%
-          trimws('both')
-      } else {
-        warning('Invalid concat.')
-        abcode[i] <- NA
-        next
-      }
-      
-      for (j in 1:length(drug.group)) {
-        drug.group[j] <-
-          ablist %>%
-          filter(.[, from] == drug.group[j]) %>%
-          select(to) %>%
-          slice(1) %>%
-          as.character()
-        if (j > 1 & to %in% c('official', 'trivial')) {
-          drug.group[j] <- drug.group[j] %>% tolower()
-        }
-      }
-      abcode[i] <- paste(drug.group, collapse = textbetween)
-    }
-  }
-  
-  if (tolower == TRUE) {
-    abcode <- abcode %>% tolower()
-  }
-  
-  abcode
-}
@@ -1,375 +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
-#' @return New class \code{rsi}
-#' @export
-#' @importFrom dplyr %>%
-#' @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)
-#' plot(rsi_data)
-#' 
-#' \donttest{
-#' library(dplyr)
-#' tbl %>%
-#'   mutate_at(vars(ends_with("_rsi")), as.rsi)
-#' sapply(mic_data, is.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()
-    x <- gsub('[^RSI]+', '', x %>% toupper())
-    # needed for UMCG in cases of "S;S" but also "S;I"; the latter will be 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 <- x %>% toupper() %>% factor(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'))
-}
-
-#' @exportMethod print.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-print.rsi <- function(x, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  cat("Class 'rsi': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA>:      ', n_total - n, '\n')
-  cat('Sum of S:  ', S, '\n')
-  cat('Sum of IR: ', IR, '\n')
-  cat('- Sum of R:', R, '\n')
-  cat('- Sum of I:', I, '\n')
-  cat('\n')
-  print(c(
-    `%S` = round((S / n) * 100, 1),
-    `%IR` = round((IR / n) * 100, 1),
-    `%I` = round((I / n) * 100, 1),
-    `%R` = round((R / n) * 100, 1)
-  ))
-}
-
-#' @exportMethod summary.rsi
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-summary.rsi <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  S <- x[x == 'S'] %>% length()
-  I <- x[x == 'I'] %>% length()
-  R <- x[x == 'R'] %>% length()
-  IR <- x[x %in% c('I', 'R')] %>% length()
-  lst <- c('rsi', n_total - n, S, IR, R, I)
-  names(lst) <- c("Mode", "<NA>", "Sum S", "Sum IR", "Sum R", "Sum I")
-  lst
-}
-
-#' @exportMethod plot.rsi
-#' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else
-#' @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('Susceptibilty Analysis of', x_name),
-       ...)
-  text(x = data$x,
-       y = data$s + 5,
-       labels = paste0(data$s, '% (n = ', data$n, ')'))
-}
-
-#' Class 'mic'
-#'
-#' This transforms a vector to a new class\code{mic}, which is an ordered factor 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 New class \code{mic}
-#' @export
-#' @importFrom dplyr %>%
-#' @examples
-#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
-#' is.mic(mic_data)
-#' plot(mic_data)
-#' 
-#' \donttest{
-#' library(dplyr)
-#' tbl %>%
-#'   mutate_at(vars(ends_with("_mic")), as.mic)
-#' sapply(mic_data, is.mic)
-#' }
-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 dot
-    x <- gsub(',', '.', x, fixed = TRUE)
-    # 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+$', '', x)
-    
-    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",
-              "<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
-              "<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
-              "<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",
-              "<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
-              "<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
-              "<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",
-              "<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
-              "<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
-              "<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
-              "<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",
-              "<0.75", "<=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",
-              "<6", "<=6", "6", ">=6", ">6",
-              "<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")
-    x <- x %>% as.character()
-    
-    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 = 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
-#' @importFrom dplyr %>%
-#' @noRd
-as.double.mic <- function(x, ...) {
-  as.double(gsub('(<=)|(>=)', '', as.character(x)))
-}
-
-#' @exportMethod as.integer.mic
-#' @export
-#' @importFrom dplyr %>%
-#' @noRd
-as.integer.mic <- function(x, ...) {
-  as.integer(gsub('(<=)|(>=)', '', as.character(x)))
-}
-
-#' @exportMethod as.numeric.mic
-#' @export
-#' @importFrom dplyr %>%
-#' @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, ...) {
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  cat("Class 'mic': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA> ', n_total - n, '\n')
-  cat('\n')
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
-  cnt <- tbl %>% pull(y)
-  names(cnt) <- tbl %>% pull(x)
-  print(cnt)
-}
-
-#' @exportMethod summary.mic
-#' @export
-#' @importFrom dplyr %>% tibble group_by summarise pull
-#' @noRd
-summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- x %>% length()
-  x <- x[!is.na(x)]
-  n <- x %>% length()
-  return(c("Mode" = 'mic',
-           "NA" = n_total - n,
-           "Min." = sort(x)[1] %>% as.character(),
-           "Max." = sort(x)[n] %>% as.character()
-  ))
-  cat("Class 'mic': ", n, " isolates\n", sep = '')
-  cat('\n')
-  cat('<NA> ', n_total - n, '\n')
-  cat('\n')
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
-  cnt <- tbl %>% pull(y)
-  names(cnt) <- tbl %>% pull(x)
-  print(cnt)
-}
-
-#' @exportMethod plot.mic
-#' @export
-#' @importFrom dplyr %>% group_by summarise
-#' @importFrom graphics plot text
-#' @noRd
-plot.mic <- function(x, ...) {
-  x_name <- deparse(substitute(x))
-  
-  data <- data.frame(mic = x, cnt = 1) %>%
-    group_by(mic) %>%
-    summarise(cnt = sum(cnt)) %>%
-    droplevels()
-
-  plot(x = data$mic,
-       y = data$cnt,
-       lwd = 2,
-       ylim = c(-0.5, max(5, max(data$cnt))),
-       ylab = 'Frequency',
-       xlab = 'MIC value',
-       main = paste('MIC values of', x_name),
-       ...)
-  text(x = data$mic,
-       y = -0.5,
-       labels = paste('n =', data$cnt))
-}
@@ -1,77 +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.                         #
-# ==================================================================== #
-
-#' Dataset with 420 antibiotics
-#'
-#' A dataset containing all antibiotics with a J0 code, with their DDD's.
-#' @format A data.frame with 420 observations and 12 variables:
-#' \describe{
-#'   \item{\code{atc}}{ATC code, like \code{J01CR02}}
-#'   \item{\code{molis}}{MOLIS code, like \code{amcl}}
-#'   \item{\code{umcg}}{UMCG code, like \code{AMCL}}
-#'   \item{\code{official}}{Official name by the WHO, like \code{"amoxicillin and enzyme inhibitor"}}
-#'   \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
-#'   \item{\code{trivial}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-#'   \item{\code{oral_ddd}}{Daily Defined Dose (DDD) according to the WHO, oral treatment}
-#'   \item{\code{oral_units}}{Units of \code{ddd_units}}
-#'   \item{\code{iv_ddd}}{Daily Defined Dose (DDD) according to the WHO, bij parenteral treatment}
-#'   \item{\code{iv_units}}{Units of \code{iv_ddd}}
-#'   \item{\code{atc_group1}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
-#'   \item{\code{atc_group2}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl} \cr \cr World Health Organization - \url{https://www.whocc.no/atc_ddd_index/}
-#' @seealso \code{\link{bactlist}}
-# todo:
-# ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 == 'Tetracyclines', FALSE, TRUE))
-# ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 %like% 'Glycopept', FALSE, useful_gramnegative))
-# Tbl1 Enterobacteriaceae are also intrinsically resistant to benzylpenicillin, glycopeptides, fusidic acid, macrolides (with some exceptions1), lincosamides, streptogramins, rifampicin, daptomycin and linezolid.
-# Tbl2 Non-fermentative Gram-negative bacteria are also generally intrinsically resistant to benzylpenicillin, first and second generation cephalosporins, glycopeptides, fusidic acid, macrolides, lincosamides, streptogramins, rifampicin, daptomycin and linezolid
-# Tbl3 Gram-negative bacteria other than Enterobacteriaceae and non-fermentative Gram-negative bacteria listed are also intrinsically resistant to glycopeptides, lincosamides, daptomycin and linezolid.
-"ablist"
-
-#' Dataset with ~2500 microorganisms
-#'
-#' A dataset containing all microorganisms of MOLIS. MO codes of the UMCG can be looked up using \code{\link{bactlist.umcg}}.
-#' @format A data.frame with 2507 observations and 10 variables:
-#' \describe{
-#'   \item{\code{bactid}}{ID of microorganism}
-#'   \item{\code{bactsys}}{Bactsyscode of microorganism}
-#'   \item{\code{family}}{Family name of microorganism}
-#'   \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
-#'   \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
-#'   \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
-#'   \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
-#'   \item{\code{type}}{Type of microorganism, like \code{"Bacterie"} en \code{"Schimmel/gist"} (these are Dutch)}
-#'   \item{\code{gramstain}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
-#'   \item{\code{aerobic}}{Type aerobe/anaerobe of bacteria}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
-#' @seealso \code{\link{ablist}} \code{\link{bactlist.umcg}}
-"bactlist"
-
-#' Translation table for UMCG with ~1100 microorganisms
-#'
-#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}.
-#' @format A data.frame with 1090 observations and 2 variables:
-#' \describe{
-#'   \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
-#'   \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}}
-#' }
-#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
-#' @seealso \code{\link{bactlist}}
-"bactlist.umcg"
@@ -1,516 +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)
-#' @param col_patid column name of the unique IDs of the patients
-#' @param col_genus column name of the genus of the microorganisms
-#' @param col_species column name of the species of the microorganisms
-#' @param col_testcode column name of the test codes, see Details
-#' @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}}.
-#' @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 (caseINsensitive)
-#' @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 the values \code{0} or \code{1})
-#' @param ignore_I ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)
-#' @param info print progress
-# @param ... parameters to pass through to \code{first_isolate}.
-#' @rdname first_isolate
-#' @details To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode. 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 is 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 selection bias.
-#'
-#'     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.
-#' @keywords isolate isolates first
-#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
-#' @return A vector to add to table, see Examples.
-#' @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,
-                          col_patid,
-                          col_genus,
-                          col_species,
-                          col_testcode = NA,
-                          col_specimen,
-                          col_icu,
-                          col_keyantibiotics = NA,
-                          episode_days = 365,
-                          testcodes_exclude = '',
-                          icu_exclude = FALSE,
-                          filter_specimen = NA,
-                          output_logical = TRUE,
-                          ignore_I = TRUE,
-                          info = TRUE) {
-  
-  # controleren of kolommen wel bestaan
-  check_columns_existance <- function(column, tblname = tbl) {
-    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
-      stop('Please check tbl for existance.')
-    }
-    
-    if (!is.na(column)) {
-      if (!(column %in% colnames(tblname))) {
-        stop('Column ', column, ' not found.')
-      }
-    }
-  }
-  
-  check_columns_existance(col_date)
-  check_columns_existance(col_patid)
-  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.na(col_testcode)) {
-    testcodes_exclude <- NA
-  }
-  # testcodes verwijderen die ingevuld zijn
-  if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
-    cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
-  }
-  
-  if (is.na(col_icu)) {
-    icu_exclude <- FALSE
-  } else {
-    tbl <- tbl %>%
-      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
-  }
-  
-  specgroup.notice <- ''
-  weighted.notice <- ''
-  # filteren op materiaalgroep en sleutelantibiotica gebruiken wanneer deze ingevuld zijn
-  if (!is.na(filter_specimen) & filter_specimen != '') {
-    check_columns_existance(col_specimen, tbl)
-    if (info == TRUE) {
-      cat('Isolates other than of specimen group \'', filter_specimen, '\' will be ignored. ', sep = '')
-    }
-  } else {
-    filter_specimen <- ''
-  }
-  if (col_keyantibiotics %in% c(NA, '')) {
-    col_keyantibiotics <- ''
-  } else {
-    tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
-  }
-  
-  if (is.na(testcodes_exclude[1])) {
-    testcodes_exclude <- ''
-  }
-  
-  # nieuwe dataframe maken met de oorspronkelijke rij-index, 0-bepaling en juiste sortering
-  #cat('Sorting table...')
-  tbl <- tbl %>%
-    mutate(first_isolate_row_index = 1:nrow(tbl),
-           eersteisolaatbepaling = 0,
-           date_lab = tbl %>% pull(col_date),
-           species = if_else(is.na(species), '', species),
-           genus = if_else(is.na(genus), '', genus))
-  
-  if (filter_specimen == '') {
-    
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_patid,
-                     col_genus,
-                     col_species,
-                     col_date))
-      row.start <- 1
-      row.end <- nrow(tbl)
-    } else {
-      if (info == TRUE) {
-        cat('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_patid,
-                     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 {
-    # sorteren op materiaal en alleen die rijen analyseren om tijd te besparen
-    if (icu_exclude == FALSE) {
-      if (info == TRUE) {
-        cat('Isolates from ICU will *NOT* be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_specimen,
-                     col_patid,
-                     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('Isolates from ICU will be ignored.\n')
-      }
-      tbl <- tbl %>%
-        arrange_at(c(col_icu,
-                     col_specimen,
-                     col_patid,
-                     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) {
-      cat('No isolates found.\n')
-    }
-    # NA's maken waar genus niet beschikbaar is
-    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))
-  }
-  
-  scope.size <- tbl %>%
-    filter(row_number() %>%
-             between(row.start,
-                     row.end),
-           genus != '') %>%
-    nrow()
-  
-  # Analyse van eerste isolaat ----
-  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))
-  
-  if (col_keyantibiotics != '') {
-    # dit duurt 2 min bij 120.000 isolaten
-    if (info == TRUE) {
-      cat('Comparing key antibiotics for first weighted isolates')
-      if (ignore_I == TRUE) {
-        cat(' (ignoring I)')
-      }
-      cat('...\n')
-    }
-    all_first <- all_first %>%
-      mutate(key_ab_lag = lag(key_ab)) %>%
-      mutate(key_ab_other = !key_antibiotics_equal(key_ab,
-                                                  key_ab_lag,
-                                                  ignore_I = ignore_I,
-                                                  info = info)) %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days
-               | key_ab_other),
-            TRUE,
-            FALSE))
-    if (info == TRUE) {
-      cat('\n')
-    }
-  } else {
-    all_first <- all_first %>%
-      mutate(
-        real_first_isolate =
-          if_else(
-            between(row_number(), row.start, row.end)
-            & genus != ''
-            & (other_pat_or_mo
-               | days_diff >= episode_days),
-            TRUE,
-            FALSE))
-  }
-  
-  # allereerst isolaat als TRUE
-  all_first[row.start, 'real_first_isolate'] <- TRUE
-  # geen testen die uitgesloten moeten worden, of ICU
-  if (!is.na(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
-  }
-  
-  # NA's maken waar genus niet beschikbaar is
-  all_first <- all_first %>%
-    mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
-  
-  all_first <- all_first %>%
-    arrange(first_isolate_row_index) %>%
-    pull(real_first_isolate)
-  
-  if (info == TRUE) {
-    cat(paste0('\nFound ',
-               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)\n'))
-  }
-  
-  if (output_logical == FALSE) {
-    all_first <- all_first %>% as.integer()
-  }
-  
-  all_first
-  
-}
-
-#' Key antibiotics based on bacteria ID
-#'
-#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
-#' @param col_bactcode column of bacteria IDs in \code{tbl}; these should occur in \code{bactlist$bactid}, see \code{\link{bactlist}}
-#' @param info print warnings
-#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics.
-#' @export
-#' @importFrom dplyr %>% mutate if_else 
-#' @return Character of length 1.
-#' @seealso \code{\link{mo_property}} \code{\link{ablist}}
-#' @examples 
-#' \donttest{
-#' #' # set key antibiotics to a new variable
-#' tbl$keyab <- key_antibiotics(tbl)
-#' }
-key_antibiotics <- function(tbl,
-                            col_bactcode = 'bacteriecode',
-                            info = TRUE,
-                            amcl = 'amcl',
-                            amox = 'amox',
-                            cfot = 'cfot',
-                            cfta = 'cfta',
-                            cftr = 'cftr',
-                            cfur = 'cfur',
-                            cipr = 'cipr',
-                            clar = 'clar',
-                            clin = 'clin',
-                            clox = 'clox',
-                            doxy = 'doxy',
-                            gent = 'gent',
-                            line = 'line',
-                            mero = 'mero',
-                            peni = 'peni',
-                            pita = 'pita',
-                            rifa = 'rifa',
-                            teic = 'teic',
-                            trsu = 'trsu',
-                            vanc = 'vanc') {
-  
-  keylist <- character(length = nrow(tbl))
-  
-  # check columns
-  col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
-                clin, clox, doxy, gent, line, mero, peni,
-                pita, rifa, teic, trsu, vanc)
-  col.list <- col.list[!is.na(col.list)]
-  if (!all(col.list %in% colnames(tbl))) {
-    if (info == TRUE) {
-      warning('These columns do not exist and will be ignored:\n',
-              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
-              immediate. = TRUE,
-              call. = FALSE)
-    }
-  }
-  
-  # bactlist aan vastknopen
-  tbl <- tbl %>% left_join_bactlist(col_bactcode)
-  
-  tbl$key_ab <- NA_character_
-  
-  # Staphylococcus
-  list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(genus == 'Staphylococcus',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # Rest of Gram +
-  list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Positi[e]?ve',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # Gram -
-  list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
-  list_ab <- list_ab[list_ab %in% colnames(tbl)]
-  tbl <- tbl %>% mutate(key_ab =
-                          if_else(gramstain %like% '^Negati[e]?ve',
-                                  apply(X = tbl[, list_ab],
-                                        MARGIN = 1,
-                                        FUN = function(x) paste(x, collapse = "")),
-                                  key_ab))
-  
-  # format
-  tbl <- tbl %>%
-    mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
-  
-  tbl$key_ab
-  
-}
-
-# Compare key antibiotics
-#
-# Check whether two text values with key antibiotics match. Supports vectors.
-# @param x,y tekst (or multiple text vectors) with antimicrobial interpretations
-# @param ignore_I ignore \code{"I"} as antimicrobial interpretation of key antibiotics (with \code{FALSE}, changes in antibiograms from S to I and I to R will be interpreted as difference)
-# @param info print progress
-# @return logical
-# @export
-# @seealso \code{\link{key_antibiotics}}
-
-# only internal use
-key_antibiotics_equal <- function(x, y, ignore_I = TRUE, info = FALSE) {
-  if (length(x) != length(y)) {
-    stop('Length of `x` and `y` must be equal.')
-  }
-  
-  result <- logical(length(x))
-  
-  if (info == TRUE) {
-    voortgang <- dplyr::progress_estimated(length(x))
-  }
-  
-  for (i in 1:length(x)) {
-    
-    if (info == TRUE) {
-      voortgang$tick()$print()
-    }
-    
-    if (is.na(x[i])) {
-      x[i] <- ''
-    }
-    if (is.na(y[i])) {
-      y[i] <- ''
-    }
-    
-    if (nchar(x[i]) != nchar(y[i])) {
-      
-      result[i] <- FALSE
-      
-    } else if (x[i] == '' & y[i] == '') {
-      
-      result[i] <- TRUE
-      
-    } else {
-      
-      x2 <- strsplit(x[i], "")[[1]]
-      y2 <- strsplit(y[i], "")[[1]]
-      
-      if (ignore_I == TRUE) {
-        valid_chars <- c('S', 's', 'R', 'r')
-      } else {
-        valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
-      }
-      
-      # Ongeldige waarden (zoals "-", NA) op beide locaties verwijderen
-      x2[which(!x2 %in% valid_chars)] <- '?'
-      x2[which(!y2 %in% valid_chars)] <- '?'
-      y2[which(!x2 %in% valid_chars)] <- '?'
-      y2[which(!y2 %in% valid_chars)] <- '?'
-      
-      result[i] <- all(x2 == y2)
-    }
-  }
-  if (info == TRUE) {
-    cat('\n')
-  }
-  result
-}
@@ -1,37 +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('.',
-                  'abname',
-                  'bactid',
-                  'cnt',
-                  'date_lab',
-                  'days_diff',
-                  'first_isolate_row_index',
-                  'genus',
-                  'gramstain',
-                  'key_ab',
-                  'key_ab_lag',
-                  'key_ab_other',
-                  'mic',
-                  'n',
-                  'other_pat_or_mo',
-                  'patient_id',
-                  'real_first_isolate',
-                  'species',
-                  'y'))
@@ -1,109 +0,0 @@
-#' Join a table with \code{bactlist}
-#'
-#' Join the list of microorganisms \code{\link{bactlist}} easily to an existing table.
-#' @rdname join
-#' @name join
-#' @aliases join inner_join
-#' @param x existing table to join
-#' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{bactlist$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{bactlist}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
-#' @param ... other parameters to pass trhough to \code{dplyr::\link[dplyr]{join}}.
-#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, 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 
-#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
-#'                             to = as.Date("2018-01-07"),
-#'                             by = 1),
-#'                  bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
-#'                                  "ESCCOL", "ESCCOL", "ESCCOL"),
-#'                  stringsAsFactors = FALSE)
-#'                  
-#' colnames(df)
-#' df2 <- left_join_bactlist(df, "bacteria_id")
-#' colnames(df2)
-inner_join_bactlist <- function(x, by = 'bactid', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::inner_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  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_bactlist <- function(x, by = 'bacteriecode', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::left_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  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_bactlist <- function(x, by = 'bacteriecode', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  join <- dplyr::right_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-  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_bactlist <- function(x, by = 'bacteriecode', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::full_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
-}
-
-#' @rdname join
-#' @export
-semi_join_bactlist <- function(x, by = 'bacteriecode', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::semi_join(x = x, y = AMR::bactlist, by = joinby, ...)
-}
-
-#' @rdname join
-#' @export
-anti_join_bactlist <- function(x, by = 'bacteriecode', ...) {
-  # no name set to `by` parameter
-  if (is.null(names(by))) {
-    joinby <- colnames(AMR::bactlist)[1]
-    names(joinby) <- by
-  } else {
-    joinby <- by
-  }
-  dplyr::anti_join(x = x, y = AMR::bactlist, by = joinby, ...)
-}
@@ -1,31 +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
-"%like%" <- function(vector, pattern) {
-  # Source: https://github.com/Rdatatable/data.table/blob/master/R/like.R
-  if (is.factor(vector)) {
-    as.integer(vector) %in% grep(pattern, levels(vector))
-  } else {
-    grepl(pattern, vector)
-  }
-}
-
-percent <- function(x, round = 1, ...) {
-  base::paste0(base::round(x * 100, digits = round), "%")
-}
@@ -1,386 +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.                         #
-# ==================================================================== #
-
-#' Resistance of isolates in data.frame
-#'
-#' \strong{NOTE: use \code{\link{rsi}} in dplyr functions like \code{\link[dplyr]{summarise}}.} \cr Calculate the percentage of S, SI, I, IR or R of a \code{data.frame} containing isolates.
-#' @param tbl \code{data.frame} containing columns with antibiotic interpretations.
-#' @param antibiotics character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{antibiotics = c("amox", "amcl")}
-#' @param interpretation antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.
-#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).
-#' @param percent return output as percent (text), will else (at default) be a double
-#' @param info calculate the amount of available isolates and print it, like \code{n = 423}
-#' @param warning show a warning when the available amount of isolates is below \code{minimum}
-#' @details Remember that you should filter your table to let it contain \strong{only first isolates}!
-#' @keywords rsi antibiotics isolate isolates
-#' @return Double or, when \code{percent = TRUE}, a character.
-#' @export
-#' @importFrom dplyr %>% n_distinct filter filter_at pull vars all_vars any_vars
-#' @seealso \code{\link{rsi}} for the function that can be used with \code{\link[dplyr]{summarise}} directly.
-#' @examples
-#' \dontrun{
-#' rsi_df(tbl_with_bloodcultures, 'amcl')
-#'
-#' rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
-#'
-#' library(dplyr)
-#' # calculate current empiric therapy of Helicobacter gastritis:
-#' my_table %>%
-#'   filter(first_isolate == TRUE, 
-#'          genus == "Helicobacter") %>%
-#'   rsi_df(antibiotics = c("amox", "metr"))
-#' }
-rsi_df <- function(tbl,
-                   antibiotics,
-                   interpretation = 'IR',
-                   minimum = 30,
-                   percent = FALSE,
-                   info = TRUE,
-                   warning = TRUE) {
-  
-  # we willen niet dat tbl$interpretation toevallig ook bestaat, dus:
-  te_testen_uitslag_ab <- interpretation
-  
-  # validatie:
-  if (min(grepl('^[a-z]{3,4}$', antibiotics)) == 0 &
-      min(grepl('^rsi[1-2]$', antibiotics)) == 0) {
-    for (i in 1:length(antibiotics)) {
-      antibiotics[i] <- paste0('rsi', i)
-    }
-  }
-  if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', te_testen_uitslag_ab)) {
-    stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
-  }
-  if ('is_ic' %in% colnames(tbl)) {
-    if (n_distinct(tbl$is_ic) > 1) {
-      warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
-    }
-  }
-  
-  # transformeren wanneer gezocht wordt op verschillende uitslagen
-  if (te_testen_uitslag_ab %in% c('SI', 'IS')) {
-    for (i in 1:length(antibiotics)) {
-      lijst <- tbl[, antibiotics[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'S'
-      }
-    }
-    te_testen_uitslag_ab <- 'S'
-  }
-  if (te_testen_uitslag_ab %in% c('RI', 'IR')) {
-    for (i in 1:length(antibiotics)) {
-      lijst <- tbl[, antibiotics[i]]
-      if ('I' %in% lijst) {
-        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'R'
-      }
-    }
-    te_testen_uitslag_ab <- 'R'
-  }
-  
-  # breuk samenstellen
-  if (length(antibiotics) == 1) {
-    numerator <- tbl %>%
-      filter(pull(., antibiotics[1]) == te_testen_uitslag_ab) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter(pull(., antibiotics[1]) %in% c("S", "I", "R")) %>%
-      nrow()
-    
-  } else if (length(antibiotics) == 2) {
-    numerator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                any_vars(. == te_testen_uitslag_ab)) %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-  } else if (length(antibiotics) == 3) {
-    numerator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                any_vars(. == te_testen_uitslag_ab)) %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-    denominator <- tbl %>%
-      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
-                all_vars(. %in% c("S", "R", "I"))) %>%
-      nrow()
-    
-  } else {
-    stop('Maximum of 3 drugs allowed.')
-  }
-  
-  # tekstdeel opbouwen
-  if (info == TRUE) {
-    cat('n =', denominator)
-    info.txt1 <- percent(denominator / nrow(tbl))
-    if (denominator == 0) {
-      info.txt1 <- 'none'
-    }
-    info.txt2 <- gsub(',', ' and',
-                      antibiotics %>%
-                        abname(to = 'trivial',
-                               tolower = TRUE) %>%
-                        toString(), fixed = TRUE)
-    info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
-    info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
-    cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
-  }
-  
-  # rekenen en opmaken
-  y <- numerator / denominator
-  if (percent == TRUE) {
-    y <- percent(y)
-  }
-  if (denominator < minimum) {
-    if (warning == TRUE) {
-      warning(paste0('TOO FEW ISOLATES OF ', toString(antibiotics), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
-    }
-    y <- NA
-  }
-  
-  # output
-  y
-}
-
-#' Resistance of isolates
-#'
-#' This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
-#' @param ab1,ab2 list with interpretations of an antibiotic
-#' @inheritParams rsi_df
-#' @details This function uses the \code{\link{rsi_df}} function internally.
-#' @keywords rsi antibiotics isolate isolates
-#' @return Double or, when \code{percent = TRUE}, a character.
-#' @export
-#' @examples
-#' \dontrun{
-#' tbl %>%
-#'   group_by(hospital) %>%
-#'   summarise(cipr = rsi(cipr))
-#'   
-#' tbl %>%
-#'   group_by(year, hospital) %>%
-#'   summarise(
-#'     isolates = n(),
-#'     cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
-#'     amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
-#'     
-#' rsi(as.rsi(isolates$amox))
-#'
-#' rsi(as.rsi(isolates$amcl), interpretation = "S")
-#' }
-rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
-  functietekst <- as.character(match.call())
-  # param 1 = functienaam
-  # param 2 = ab1
-  # param 3 = ab2
-  ab1.naam <- functietekst[2]
-  if (!grepl('^[a-z]{3,4}$', ab1.naam)) {
-    ab1.naam <- 'rsi1'
-  }
-  ab2.naam <- functietekst[3]
-  if (!grepl('^[a-z]{3,4}$', ab2.naam)) {
-    ab2.naam <- 'rsi2'
-  }
-  
-  tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
-  
-  colnames(tbl) <- c(ab1.naam, ab2.naam)
-  
-  if (length(ab2) == 1) {
-    return(rsi_df(tbl = tbl,
-                  antibiotics = ab1.naam,
-                  interpretation = interpretation,
-                  minimum = minimum,
-                  percent = percent,
-                  info = info,
-                  warning = warning))
-  } else {
-    if (length(ab1) != length(ab2)) {
-      stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
-    }
-    if (interpretation != 'S') {
-      warning('`interpretation` is not set to S, albeit analysing a combination therapy.')
-    }
-    return(rsi_df(tbl = tbl,
-                  antibiotics = c(ab1.naam, ab2.naam),
-                  interpretation = interpretation,
-                  minimum = minimum,
-                  percent = percent,
-                  info = info,
-                  warning = warning))
-  }
-}
-
-#' 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}.
-#' @param tbl table that contains columns \code{col_ab} and \code{col_date}
-#' @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
-#' @param year_max highest year to use in the prediction model, deafults to 15 years after today
-#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
-#' @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 overwrite predictions of years that are actually available in the data, 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 \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
-#' @seealso \code{\link{lm}} \cr \code{\link{glm}}
-#' @export
-#' @importFrom dplyr %>% pull mutate group_by_at summarise filter
-#' @importFrom reshape2 dcast
-#' @examples
-#' \dontrun{
-#' # use it directly:
-#' rsi_predict(tbl[which(first_isolate == TRUE & genus == "Haemophilus"),], "amcl")
-#'   
-#' # or with dplyr so you can actually read it:
-#' library(dplyr)
-#' tbl %>%
-#'   filter(first_isolate == TRUE,
-#'          genus == "Haemophilus") %>%
-#'   rsi_predict("amcl")
-#'
-#' tbl %>%
-#'   filter(first_isolate_weighted == TRUE,
-#'          genus == "Haemophilus") %>%
-#'   rsi_predict(col_ab = "amcl",
-#'               year_max = 2050,
-#'               year_every = 5)
-#'
-#' }
-rsi_predict <- function(tbl,
-                        col_ab,
-                        col_date = 'ontvangstdatum',
-                        year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
-                        year_every = 1,
-                        model = 'binomial',
-                        I_as_R = TRUE,
-                        preserve_measurements = TRUE,
-                        info = TRUE) {
-  
-  if (I_as_R == TRUE) {
-    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
-  }
-  
-  year <- function(x) {
-    as.integer(format(as.Date(x), '%Y'))
-  }
-  
-  years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
-  
-  df <- tbl %>%
-    mutate(year = year(tbl %>% pull(col_date))) %>%
-    group_by_at(c('year', col_ab)) %>%
-    summarise(n())
-  colnames(df) <- c('year', 'antibiotic', 'count')
-  df <- df %>%
-    reshape2::dcast(year ~ antibiotic, value.var = 'count')
-  
-  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 <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else if (model == 'loglin') {
-    loglinmodel <- with(df, glm(R ~ year, family = poisson))
-    if (info == TRUE) {
-      cat('\nLog-linear regression model (loglin) with poisson distribution')
-      cat('\n--------------------------------------------------------------\n')
-      print(summary(loglinmodel))
-    }
-    
-    predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else if (model %in% c('lin', 'linear')) {
-    linmodel <- with(df, lm((R / (R + S)) ~ year))
-    if (info == TRUE) {
-      cat('\nLinear regression model')
-      cat('\n-----------------------\n')
-      print(summary(linmodel))
-    }
-    
-    predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
-    prediction <- predictmodel$fit
-    se <- predictmodel$se.fit
-    
-  } else {
-    stop('No valid model selected.')
-  }
-  
-  # prepare the output dataframe
-  prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
-  
-  prediction$se_min <- prediction$probR - se
-  prediction$se_max <- prediction$probR + se
-  
-  if (model == 'loglin') {
-    prediction$probR <- prediction$probR %>%
-      format(scientific = FALSE) %>%
-      as.integer()
-    prediction$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$se_min < 0)] <- 0
-  
-  total <- prediction
-  
-  if (preserve_measurements == TRUE) {
-    # geschatte data vervangen door gemeten data
-    if (I_as_R == TRUE) {
-      if (!'I' %in% colnames(df)) {
-        df$I <- 0
-      }
-      df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
-    } else {
-      df$probR <- df$R / rowSums(df[, c('R', 'S')])
-    }
-    measurements <- data.frame(year = df$year,
-                           probR = df$probR,
-                           se_min = NA,
-                           se_max = NA,
-                           stringsAsFactors = FALSE)
-    colnames(measurements) <- colnames(prediction)
-    prediction <- prediction %>% filter(!year %in% df$year)
-    
-    total <- rbind(measurements, prediction)
-  }
-  
-  total
-  
-}
@@ -1,147 +0,0 @@
-# `AMR`
-This is an [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).
-
-![logo_uni](man/figures/logo_en.png)![logo_umcg](man/figures/logo_umcg.png)
-
-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 department of the University Medical Center Groningen (UMCG). They also maintain this package, see [Authors](#authors).
-
-## Why this package?
-This R package contains functions to make microbiological, epidemiological data analysis easier. It allows the use of some new S3 classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
-
-AMR can also be predicted for the forthcoming years with the `rsi_predict` function. For use with the `dplyr` package, the `rsi` function can be used in conjunction with `summarise` to calculate the resistance percentages of different antibiotic columns of a table.
-
-It also contains functions to translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) and vice versa.
-
-## How to get it?
-[![CRAN_Badge](http://www.r-pkg.org/badges/version/AMR)](http://cran.r-project.org/package=AMR)
-
-This package is available on CRAN (latest stable version) and also here on GitHub (latest development version).
-
-#### Latest stable version from CRAN (recommended)
-RStudio:
-  Click on `Tools` and then `Install Packages..`
-  Type in `AMR` and press <kbd>Install</kbd>
-
-Other:
-```r
-install.packages("AMR")
-```
-
-#### Latest development version from GitHub
-```r
-devtools::install_github("msberends/AMR")
-```
-
-## How to use it?
-```r
-# Call it with:
-library(AMR)
-
-# For a list of functions:
-help(package = "AMR")
-```
-
-### Databases included in package
-```r
-# Dataset with ATC antibiotics codes, official names and DDD's (oral and parenteral)
-ablist        # A tibble: 420 x 12
-
-# Dataset with bacteria codes and properties like gram stain and aerobic/anaerobic
-bactlist      # A tibble: 2,507 x 10
-```
-
-### 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`.
-
-```r
-# Transform values to new classes
-mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-```
-These functions also try to coerce valid values.
-
-Quick overviews when just printing objects:
-```r
-mic_data
-# Class 'mic': 7 isolates
-# 
-# <NA>  0
-# 
-# <=0.128       1       8      16    >=32
-#       1       1       2       2       1
-
-rsi_data
-# Class 'rsi': 880 isolates
-# 
-# <NA>:       0 
-# Sum of S:   474 
-# Sum of IR:  406 
-# - Sum of R: 370 
-# - Sum of I: 36 
-# 
-#   %S  %IR   %I   %R 
-# 53.9 46.1  4.1 42.0 
-```
-
-A plot of `rsi_data`:
-```r
-plot(rsi_data)
-```
-
-![example](man/figures/rsi_example.png)
-
-Other epidemiological functions:
-
-```r
-# Apply EUCAST Expert Rules v3.1 (latest) to antibiotic columns
-EUCAST_rules(...)
-
-# Determine key antibiotic based on bacteria ID
-key_antibiotics(...)
-# Check if key antibiotics are equal
-key_antibiotics_equal(...)
-
-# Selection of first isolates of any patient
-first_isolate(...)
-
-# Calculate resistance levels of antibiotics
-rsi(...)
-# Predict resistance levels of antibiotics
-rsi_predict(...)
-
-# Get name of antibiotic by ATC code
-abname(...)
-abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
-```
-
-## Authors
-
-  - [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
-  - [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
-  - [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
-  
-<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
-
-<sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands
-
-## Copyright
-This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
-
- May be used for commercial purposes
-
- May be used for private 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
@@ -0,0 +1,660 @@
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+    <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9281</small>
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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>
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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>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>
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+</div>
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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="../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="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</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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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9281</small>
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+<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>
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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>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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+</html>
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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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+    <div class="section ">
+      <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>
+        <dd>
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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>
+
+
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+
+    <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="">2.1.1.9281</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