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<a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
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articles/welcome_to_AMR.html
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authors.html
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index.html
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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="">2.1.1.9063</small>
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news/index.html
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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="">2.1.1.9063</small>
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@@ -53,18 +53,18 @@
</div>
<div class="section level2">
<h2 class="pkg-version" data-toc-text="2.1.1.9063" id="amr-2119063">AMR 2.1.1.9063<a class="anchor" aria-label="anchor" href="#amr-2119063"></a></h2>
<h2 class="pkg-version" data-toc-text="2.1.1.9066" id="amr-2119066">AMR 2.1.1.9066<a class="anchor" aria-label="anchor" href="#amr-2119066"></a></h2>
<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
<div class="section level5">
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9063">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9063"></a></h5>
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9066">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9066"></a></h5>
<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
</div>
<div class="section level3">
<h3 id="breaking-2-1-1-9063">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9063"></a></h3>
<h3 id="breaking-2-1-1-9066">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9066"></a></h3>
<ul><li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over a year ago</li>
</ul></div>
<div class="section level3">
<h3 id="new-2-1-1-9063">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9063"></a></h3>
<h3 id="new-2-1-1-9066">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9066"></a></h3>
<ul><li>One Health implementation
<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for animal breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
<li>The <code>clinical_breakpoints</code> data set contains all these breakpoints, and can be downloaded on our <a href="https://msberends.github.io/AMR/articles/datasets.html">download page</a>.</li>
@@ -97,7 +97,7 @@
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="changed-2-1-1-9063">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9063"></a></h3>
<h3 id="changed-2-1-1-9066">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9066"></a></h3>
<ul><li>SIR interpretation
<ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
<li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
@@ -137,7 +137,7 @@
<li>Fixed a bug for when <code><a href="../reference/antibiogram.html">antibiogram()</a></code> returns an empty data set</li>
</ul></div>
<div class="section level3">
<h3 id="other-2-1-1-9063">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9063"></a></h3>
<h3 id="other-2-1-1-9066">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9066"></a></h3>
<ul><li>Added Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input</li>
</ul></div>
</div>
pkgdown.yml
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resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2024-07-16T12:57Z
last_built: 2024-07-16T14:13Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
reference/AMR-options.html
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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="">2.1.1.9063</small>
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reference/AMR.html
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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="">2.1.1.9063</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9066</small>
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reference/WHOCC.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/WHONET.html
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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="">2.1.1.9063</small>
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reference/ab_from_text.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/ab_property.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/add_custom_antimicrobials.html
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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="">2.1.1.9063</small>
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reference/add_custom_microorganisms.html
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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="">2.1.1.9063</small>
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reference/age.html
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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="">2.1.1.9063</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9066</small>
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reference/age_groups.html
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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="">2.1.1.9063</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9066</small>
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reference/antibiogram.html
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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="">2.1.1.9063</small>
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reference/antibiotic_class_selectors.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/antibiotics.html
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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="">2.1.1.9063</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9066</small>
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reference/as.ab.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/as.av.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/as.disk.html
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/as.mic.html
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reference/as.mo.html
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@@ -7,7 +7,7 @@
<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="">2.1.1.9063</small>
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@@ -158,7 +158,7 @@
<p>Values that cannot be coerced will be considered 'unknown' and will be returned as the MO code <code>UNKNOWN</code> with a warning.</p>
<p>Use the <code><a href="mo_property.html">mo_*</a></code> functions to get properties based on the returned code, see <em>Examples</em>.</p>
<p>The <code>as.mo()</code> function uses a novel <a href="mo_matching_score.html">matching score algorithm</a> (see <em>Matching Score for Microorganisms</em> below) to match input against the <a href="microorganisms.html">available microbial taxonomy</a> in this package. This will lead to the effect that e.g. <code>"E. coli"</code> (a microorganism highly prevalent in humans) will return the microbial ID of <em>Escherichia coli</em> and not <em>Entamoeba coli</em> (a microorganism less prevalent in humans), although the latter would alphabetically come first.</p>
<p>With <code>Becker = TRUE</code>, the following 85 staphylococci will be converted to the <strong>coagulase-negative group</strong>: <em>S. argensis</em>, <em>S. arlettae</em>, <em>S. auricularis</em>, <em>S. borealis</em>, <em>S. caeli</em>, <em>S. caledonicus</em>, <em>S. canis</em>, <em>S. capitis</em>, <em>S. capitis capitis</em>, <em>S. capitis urealyticus</em>, <em>S. capitis ureolyticus</em>, <em>S. caprae</em>, <em>S. carnosus</em>, <em>S. carnosus carnosus</em>, <em>S. carnosus utilis</em>, <em>S. casei</em>, <em>S. caseolyticus</em>, <em>S. chromogenes</em>, <em>S. cohnii</em>, <em>S. cohnii cohnii</em>, <em>S. cohnii urealyticum</em>, <em>S. cohnii urealyticus</em>, <em>S. condimenti</em>, <em>S. croceilyticus</em>, <em>S. debuckii</em>, <em>S. devriesei</em>, <em>S. durrellii</em>, <em>S. edaphicus</em>, <em>S. epidermidis</em>, <em>S. equorum</em>, <em>S. equorum equorum</em>, <em>S. equorum linens</em>, <em>S. felis</em>, <em>S. fleurettii</em>, <em>S. gallinarum</em>, <em>S. haemolyticus</em>, <em>S. hominis</em>, <em>S. hominis hominis</em>, <em>S. hominis novobiosepticus</em>, <em>S. jettensis</em>, <em>S. kloosii</em>, <em>S. lentus</em>, <em>S. lloydii</em>, <em>S. lugdunensis</em>, <em>S. massiliensis</em>, <em>S. microti</em>, <em>S. muscae</em>, <em>S. nepalensis</em>, <em>S. pasteuri</em>, <em>S. petrasii</em>, <em>S. petrasii croceilyticus</em>, <em>S. petrasii jettensis</em>, <em>S. petrasii petrasii</em>, <em>S. petrasii pragensis</em>, <em>S. pettenkoferi</em>, <em>S. piscifermentans</em>, <em>S. pragensis</em>, <em>S. pseudoxylosus</em>, <em>S. pulvereri</em>, <em>S. ratti</em>, <em>S. rostri</em>, <em>S. saccharolyticus</em>, <em>S. saprophyticus</em>, <em>S. saprophyticus bovis</em>, <em>S. saprophyticus saprophyticus</em>, <em>S. schleiferi</em>, <em>S. schleiferi schleiferi</em>, <em>S. sciuri</em>, <em>S. sciuri carnaticus</em>, <em>S. sciuri lentus</em>, <em>S. sciuri rodentium</em>, <em>S. sciuri sciuri</em>, <em>S. simulans</em>, <em>S. stepanovicii</em>, <em>S. succinus</em>, <em>S. succinus casei</em>, <em>S. succinus succinus</em>, <em>S. taiwanensis</em>, <em>S. urealyticus</em>, <em>S. ureilyticus</em>, <em>S. veratri</em>, <em>S. vitulinus</em>, <em>S. vitulus</em>, <em>S. warneri</em>, and <em>S. xylosus</em>.<br> The following 16 staphylococci will be converted to the <strong>coagulase-positive group</strong>: <em>S. agnetis</em>, <em>S. argenteus</em>, <em>S. coagulans</em>, <em>S. cornubiensis</em>, <em>S. delphini</em>, <em>S. hyicus</em>, <em>S. hyicus chromogenes</em>, <em>S. hyicus hyicus</em>, <em>S. intermedius</em>, <em>S. lutrae</em>, <em>S. pseudintermedius</em>, <em>S. roterodami</em>, <em>S. schleiferi coagulans</em>, <em>S. schweitzeri</em>, <em>S. simiae</em>, and <em>S. singaporensis</em>.</p>
<p>With <code>Becker = TRUE</code>, the following 89 staphylococci will be converted to the <strong>coagulase-negative group</strong>: <em>S. americanisciuri</em>, <em>S. argensis</em>, <em>S. arlettae</em>, <em>S. auricularis</em>, <em>S. borealis</em>, <em>S. brunensis</em>, <em>S. caeli</em>, <em>S. caledonicus</em>, <em>S. canis</em>, <em>S. capitis</em>, <em>S. capitis capitis</em>, <em>S. capitis urealyticus</em>, <em>S. capitis ureolyticus</em>, <em>S. caprae</em>, <em>S. carnosus</em>, <em>S. carnosus carnosus</em>, <em>S. carnosus utilis</em>, <em>S. casei</em>, <em>S. caseolyticus</em>, <em>S. chromogenes</em>, <em>S. cohnii</em>, <em>S. cohnii cohnii</em>, <em>S. cohnii urealyticum</em>, <em>S. cohnii urealyticus</em>, <em>S. condimenti</em>, <em>S. croceilyticus</em>, <em>S. debuckii</em>, <em>S. devriesei</em>, <em>S. durrellii</em>, <em>S. edaphicus</em>, <em>S. epidermidis</em>, <em>S. equorum</em>, <em>S. equorum equorum</em>, <em>S. equorum linens</em>, <em>S. felis</em>, <em>S. fleurettii</em>, <em>S. gallinarum</em>, <em>S. haemolyticus</em>, <em>S. hominis</em>, <em>S. hominis hominis</em>, <em>S. hominis novobiosepticus</em>, <em>S. jettensis</em>, <em>S. kloosii</em>, <em>S. lentus</em>, <em>S. lloydii</em>, <em>S. lugdunensis</em>, <em>S. marylandisciuri</em>, <em>S. massiliensis</em>, <em>S. microti</em>, <em>S. muscae</em>, <em>S. nepalensis</em>, <em>S. pasteuri</em>, <em>S. petrasii</em>, <em>S. petrasii croceilyticus</em>, <em>S. petrasii jettensis</em>, <em>S. petrasii petrasii</em>, <em>S. petrasii pragensis</em>, <em>S. pettenkoferi</em>, <em>S. piscifermentans</em>, <em>S. pragensis</em>, <em>S. pseudoxylosus</em>, <em>S. pulvereri</em>, <em>S. ratti</em>, <em>S. rostri</em>, <em>S. saccharolyticus</em>, <em>S. saprophyticus</em>, <em>S. saprophyticus bovis</em>, <em>S. saprophyticus saprophyticus</em>, <em>S. schleiferi</em>, <em>S. schleiferi schleiferi</em>, <em>S. sciuri</em>, <em>S. sciuri carnaticus</em>, <em>S. sciuri lentus</em>, <em>S. sciuri rodentium</em>, <em>S. sciuri sciuri</em>, <em>S. shinii</em>, <em>S. simulans</em>, <em>S. stepanovicii</em>, <em>S. succinus</em>, <em>S. succinus casei</em>, <em>S. succinus succinus</em>, <em>S. taiwanensis</em>, <em>S. urealyticus</em>, <em>S. ureilyticus</em>, <em>S. veratri</em>, <em>S. vitulinus</em>, <em>S. vitulus</em>, <em>S. warneri</em>, and <em>S. xylosus</em>.<br> The following 16 staphylococci will be converted to the <strong>coagulase-positive group</strong>: <em>S. agnetis</em>, <em>S. argenteus</em>, <em>S. coagulans</em>, <em>S. cornubiensis</em>, <em>S. delphini</em>, <em>S. hyicus</em>, <em>S. hyicus chromogenes</em>, <em>S. hyicus hyicus</em>, <em>S. intermedius</em>, <em>S. lutrae</em>, <em>S. pseudintermedius</em>, <em>S. roterodami</em>, <em>S. schleiferi coagulans</em>, <em>S. schweitzeri</em>, <em>S. simiae</em>, and <em>S. singaporensis</em>.</p>
<p>With <code>Lancefield = TRUE</code>, the following streptococci will be converted to their corresponding Lancefield group: <em>S. agalactiae</em> (Group B), <em>S. anginosus anginosus</em> (Group F), <em>S. anginosus whileyi</em> (Group F), <em>S. anginosus</em> (Group F), <em>S. canis</em> (Group G), <em>S. dysgalactiae dysgalactiae</em> (Group C), <em>S. dysgalactiae equisimilis</em> (Group C), <em>S. dysgalactiae</em> (Group C), <em>S. equi equi</em> (Group C), <em>S. equi ruminatorum</em> (Group C), <em>S. equi zooepidemicus</em> (Group C), <em>S. equi</em> (Group C), <em>S. pyogenes</em> (Group A), <em>S. salivarius salivarius</em> (Group K), <em>S. salivarius thermophilus</em> (Group K), <em>S. salivarius</em> (Group K), and <em>S. sanguinis</em> (Group H).</p><div class="section">
<h3 id="coping-with-uncertain-results">Coping with Uncertain Results<a class="anchor" aria-label="anchor" href="#coping-with-uncertain-results"></a></h3>
reference/as.sir.html
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@@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
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@@ -760,16 +760,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 59 × 16</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index ab_given mo_given host_given ab mo </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-07-16 <span style="color: #949494;">12:58:17</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-07-16 <span style="color: #949494;">12:58:23</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-07-16 <span style="color: #949494;">12:58:24</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-07-16 <span style="color: #949494;">12:58:24</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-07-16 <span style="color: #949494;">12:58:25</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-07-16 <span style="color: #949494;">12:58:17</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-07-16 <span style="color: #949494;">12:58:23</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-07-16 <span style="color: #949494;">12:58:24</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-07-16 <span style="color: #949494;">12:58:24</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-07-16 <span style="color: #949494;">12:58:25</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-07-16 <span style="color: #949494;">14:14:03</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-07-16 <span style="color: #949494;">14:14:04</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-07-16 <span style="color: #949494;">14:14:04</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-07-16 <span style="color: #949494;">14:14:05</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-07-16 <span style="color: #949494;">14:14:03</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-07-16 <span style="color: #949494;">14:14:04</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-07-16 <span style="color: #949494;">14:14:04</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-07-16 <span style="color: #949494;">14:14:05</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-07-16 <span style="color: #949494;">14:14:03</span> 2 cipro Escheri… human CIP B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-07-16 <span style="color: #949494;">14:14:04</span> 2 cipro Escheri… human CIP B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 49 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;dbl&gt;, outcome &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>
reference/atc_online.html
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reference/availability.html
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@@ -13,7 +13,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> ClLine truncated
[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> ClLine truncated