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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 15 January 2025.</p>
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generated on 16 January 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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@@ -144,21 +144,21 @@ 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">2025-01-15</td>
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<td align="center">2025-01-16</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">2025-01-15</td>
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<td align="center">2025-01-16</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">2025-01-15</td>
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<td align="center">2025-01-16</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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@@ -140,7 +140,7 @@ package.</p>
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<span><span class="co">#> <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">filter()</span> masks <span style="color: #0000BB;">stats</span>::filter()</span></span>
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<span><span class="co">#> <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">lag()</span> masks <span style="color: #0000BB;">stats</span>::lag()</span></span>
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<span><span class="co">#> <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">recipes</span>::<span style="color: #00BB00;">step()</span> masks <span style="color: #0000BB;">stats</span>::step()</span></span>
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<span><span class="co">#> <span style="color: #0000BB;">•</span> Dig deeper into tidy modeling with R at <span style="color: #00BB00;">https://www.tmwr.org</span></span></span>
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<span><span class="co">#> <span style="color: #0000BB;">•</span> Learn how to get started at <span style="color: #00BB00;">https://www.tidymodels.org/start/</span></span></span>
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<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/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># For AMR data analysis</span></span>
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<span></span>
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<span><span class="co"># Load the example_isolates dataset</span></span>
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@@ -77,7 +77,7 @@
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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>Data sets for download / own use</h1>
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<h4 data-toc-skip class="date">15 January 2025</h4>
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<h4 data-toc-skip class="date">16 January 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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resistance_predict: resistance_predict.html
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welcome_to_AMR: welcome_to_AMR.html
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WHONET: WHONET.html
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last_built: 2025-01-15T15:21Z
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last_built: 2025-01-16T11:00Z
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urls:
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reference: https://msberends.github.io/AMR/reference
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article: https://msberends.github.io/AMR/articles
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@@ -111,16 +111,16 @@
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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 1965-12-05 59 59.11233 34</span>
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<span class="r-out co"><span class="r-pr">#></span> 2 1980-03-01 44 44.87671 19</span>
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<span class="r-out co"><span class="r-pr">#></span> 3 1949-11-01 75 75.20548 50</span>
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<span class="r-out co"><span class="r-pr">#></span> 4 1947-02-14 77 77.91781 52</span>
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<span class="r-out co"><span class="r-pr">#></span> 5 1940-02-19 84 84.90411 59</span>
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<span class="r-out co"><span class="r-pr">#></span> 6 1988-01-10 37 37.01370 11</span>
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<span class="r-out co"><span class="r-pr">#></span> 7 1997-08-27 27 27.38630 2</span>
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<span class="r-out co"><span class="r-pr">#></span> 8 1978-01-26 46 46.96986 21</span>
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<span class="r-out co"><span class="r-pr">#></span> 9 1972-06-17 52 52.58082 27</span>
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<span class="r-out co"><span class="r-pr">#></span> 10 1986-08-10 38 38.43288 13</span>
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<span class="r-out co"><span class="r-pr">#></span> 1 1965-12-05 59 59.11507 34</span>
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<span class="r-out co"><span class="r-pr">#></span> 2 1980-03-01 44 44.87945 19</span>
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<span class="r-out co"><span class="r-pr">#></span> 3 1949-11-01 75 75.20822 50</span>
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<span class="r-out co"><span class="r-pr">#></span> 4 1947-02-14 77 77.92055 52</span>
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<span class="r-out co"><span class="r-pr">#></span> 5 1940-02-19 84 84.90685 59</span>
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<span class="r-out co"><span class="r-pr">#></span> 6 1988-01-10 37 37.01644 11</span>
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<span class="r-out co"><span class="r-pr">#></span> 7 1997-08-27 27 27.38904 2</span>
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<span class="r-out co"><span class="r-pr">#></span> 8 1978-01-26 46 46.97260 21</span>
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<span class="r-out co"><span class="r-pr">#></span> 9 1972-06-17 52 52.58356 27</span>
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<span class="r-out co"><span class="r-pr">#></span> 10 1986-08-10 38 38.43562 13</span>
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</code></pre></div>
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</div>
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</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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@@ -786,16 +786,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 57 × 16</span></span>
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<span class="r-out co"><span class="r-pr">#></span> datetime index ab_given mo_given host_given ab mo </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">*</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;"><ab></span> <span style="color: #949494; font-style: italic;"><mo></span> </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> 2025-01-15 <span style="color: #949494;">15:22:23</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> 2025-01-15 <span style="color: #949494;">15:22:31</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> 2025-01-15 <span style="color: #949494;">15:22:31</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 4</span> 2025-01-15 <span style="color: #949494;">15:22:32</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> 2025-01-15 <span style="color: #949494;">15:22:32</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2025-01-15 <span style="color: #949494;">15:22:23</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> 2025-01-15 <span style="color: #949494;">15:22:31</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 8</span> 2025-01-15 <span style="color: #949494;">15:22:31</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 9</span> 2025-01-15 <span style="color: #949494;">15:22:32</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">10</span> 2025-01-15 <span style="color: #949494;">15:22:32</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> 2025-01-16 <span style="color: #949494;">11:00:46</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> 2025-01-16 <span style="color: #949494;">11:00:53</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> 2025-01-16 <span style="color: #949494;">11:00:53</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 4</span> 2025-01-16 <span style="color: #949494;">11:00:54</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> 2025-01-16 <span style="color: #949494;">11:00:54</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2025-01-16 <span style="color: #949494;">11:00:46</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> 2025-01-16 <span style="color: #949494;">11:00:53</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 8</span> 2025-01-16 <span style="color: #949494;">11:00:53</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 9</span> 2025-01-16 <span style="color: #949494;">11:00:54</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;">10</span> 2025-01-16 <span style="color: #949494;">11:00:54</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 47 more rows</span></span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 9 more variables: host <chr>, method <chr>, input <dbl>, outcome <sir>,</span></span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,</span></span>
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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
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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
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Reference in new issue
Block a user