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@@ -31,7 +31,7 @@
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<a class="navbar-brand me-2" href="https://amr-for-r.org/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="">3.0.0.9032</small>
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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="">3.0.0.9032</small>
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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="">3.0.0.9032</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9033</small>
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website update since they are based on randomly created values and the
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page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
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Markdown</a>. However, the methodology remains unchanged. This page was
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generated on 13 September 2025.</p>
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generated on 15 September 2025.</p>
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<div class="section level2">
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<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
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</h2>
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@@ -147,21 +147,21 @@ make the structure of your data generally look like this:</p>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td align="center">2025-09-13</td>
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<td align="center">2025-09-15</td>
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<td align="center">abcd</td>
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<td align="center">Escherichia coli</td>
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<td align="center">S</td>
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<td align="center">S</td>
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</tr>
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<tr class="even">
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<td align="center">2025-09-13</td>
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<td align="center">2025-09-15</td>
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<td align="center">abcd</td>
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<td align="center">Escherichia coli</td>
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<td align="center">S</td>
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<td align="center">R</td>
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</tr>
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<tr class="odd">
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<td align="center">2025-09-13</td>
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<td align="center">2025-09-15</td>
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<td align="center">efgh</td>
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<td align="center">Escherichia coli</td>
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<td align="center">R</td>
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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="">3.0.0.9032</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9033</small>
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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="">3.0.0.9032</small>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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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="">3.0.0.9032</small>
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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="">3.0.0.9032</small>
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
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<h4 data-toc-skip class="date">13 September 2025</h4>
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<h4 data-toc-skip class="date">15 September 2025</h4>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
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<div class="d-none name"><code>datasets.Rmd</code></div>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9032</small>
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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="">3.0.0.9032</small>
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</div>
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<div class="section level2">
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<h2 class="pkg-version" data-toc-text="3.0.0.9032" id="amr-3009032">AMR 3.0.0.9032<a class="anchor" aria-label="anchor" href="#amr-3009032"></a></h2>
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<h2 class="pkg-version" data-toc-text="3.0.0.9033" id="amr-3009033">AMR 3.0.0.9033<a class="anchor" aria-label="anchor" href="#amr-3009033"></a></h2>
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<p>This is a bugfix release following the release of v3.0.0 in June 2025.</p>
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<div class="section level4">
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<h4 id="changed-3-0-0-9032">Changed<a class="anchor" aria-label="anchor" href="#changed-3-0-0-9032"></a></h4>
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<h4 id="changed-3-0-0-9033">Changed<a class="anchor" aria-label="anchor" href="#changed-3-0-0-9033"></a></h4>
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<ul><li>Fixed bugs introduced by <code>ggplot2</code> v4.0.0 (<a href="https://github.com/msberends/AMR/issues/236" class="external-link">#236</a>)</li>
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<li>Fixed a bug in <code><a href="../reference/antibiogram.html">antibiogram()</a></code> for when no antimicrobials are set</li>
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<li>Fixed a bug in <code><a href="../reference/antibiogram.html">antibiogram()</a></code> to allow column names containing the <code>+</code> character (<a href="https://github.com/msberends/AMR/issues/222" class="external-link">#222</a>)</li>
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PCA: PCA.html
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WHONET: WHONET.html
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WISCA: WISCA.html
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last_built: 2025-09-13T12:08Z
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last_built: 2025-09-15T07:15Z
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urls:
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reference: https://amr-for-r.org/reference
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article: https://amr-for-r.org/articles
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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="">3.0.0.9032</small>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<span class="r-in"><span></span></span>
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<span class="r-in"><span><span class="va">df</span></span></span>
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<span class="r-out co"><span class="r-pr">#></span> birth_date age age_exact age_at_y2k</span>
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<span class="r-out co"><span class="r-pr">#></span> 1 1980-02-27 45 45.54247 19</span>
|
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<span class="r-out co"><span class="r-pr">#></span> 2 1953-07-26 72 72.13425 46</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 3 1949-09-02 76 76.03014 50</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 4 1986-08-03 39 39.11233 13</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 5 1932-11-19 92 92.81644 67</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 6 1949-03-30 76 76.45753 50</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 7 1996-06-23 29 29.22466 3</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 8 1963-09-16 61 61.99178 36</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 9 1952-05-16 73 73.32877 47</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 10 1952-11-14 72 72.83014 47</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 1 1980-02-27 45 45.54795 19</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 2 1953-07-26 72 72.13973 46</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 3 1949-09-02 76 76.03562 50</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 4 1986-08-03 39 39.11781 13</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 5 1932-11-19 92 92.82192 67</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 6 1949-03-30 76 76.46301 50</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 7 1996-06-23 29 29.23014 3</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 8 1963-09-16 61 61.99726 36</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 9 1952-05-16 73 73.33425 47</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> 10 1952-11-14 72 72.83562 47</span>
|
||||
</code></pre></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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||||
@@ -7,7 +7,7 @@
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||||
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@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9032</small>
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@@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
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@@ -415,10 +415,10 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 4 × 18</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> datetime index method ab_given mo_given host_given input_given</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494; font-style: italic;"><dttm></span> <span style="color: #949494; font-style: italic;"><int></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> 2025-09-13 <span style="color: #949494;">12:09:10</span> 1 MIC amoxicillin Escherich… human 8 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> 2025-09-13 <span style="color: #949494;">12:09:11</span> 1 MIC cipro Escherich… human 0.256 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">3</span> 2025-09-13 <span style="color: #949494;">12:09:11</span> 1 DISK tobra Escherich… human 16 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">4</span> 2025-09-13 <span style="color: #949494;">12:09:11</span> 1 DISK genta Escherich… human 18 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">1</span> 2025-09-15 <span style="color: #949494;">07:16:09</span> 1 MIC amoxicillin Escherich… human 8 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">2</span> 2025-09-15 <span style="color: #949494;">07:16:09</span> 1 MIC cipro Escherich… human 0.256 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">3</span> 2025-09-15 <span style="color: #949494;">07:16:10</span> 1 DISK tobra Escherich… human 16 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">4</span> 2025-09-15 <span style="color: #949494;">07:16:10</span> 1 DISK genta Escherich… human 18 </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># breakpoint_S_R <chr>, site <chr></span></span>
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||||
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9032</small>
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||||
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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||||
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@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9032</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.0.9033</small>
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||||
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||||
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@@ -7,7 +7,7 @@
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||||
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|
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[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"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://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"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://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"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://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"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\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] Line truncated
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[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"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://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"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://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"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://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"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\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] Line truncated
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Block a user