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@@ -205,7 +205,7 @@
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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 19 June 2024.</p>
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generated on 23 June 2024.</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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@@ -261,21 +261,21 @@ 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">2024-06-19</td>
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<td align="center">2024-06-23</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">2024-06-19</td>
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<td align="center">2024-06-23</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">2024-06-19</td>
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<td align="center">2024-06-23</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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@@ -194,7 +194,7 @@
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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">19 June 2024</h4>
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<h4 data-toc-skip class="date">23 June 2024</h4>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/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: 2024-06-19T13:44Z
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last_built: 2024-06-23T14:21Z
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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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@@ -222,16 +222,16 @@
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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 58 58.53825 34</span>
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<span class="r-out co"><span class="r-pr">#></span> 2 1980-03-01 44 44.30055 19</span>
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<span class="r-out co"><span class="r-pr">#></span> 3 1949-11-01 74 74.63115 50</span>
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<span class="r-out co"><span class="r-pr">#></span> 4 1947-02-14 77 77.34426 52</span>
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<span class="r-out co"><span class="r-pr">#></span> 5 1940-02-19 84 84.33060 59</span>
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<span class="r-out co"><span class="r-pr">#></span> 6 1988-01-10 36 36.43989 11</span>
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<span class="r-out co"><span class="r-pr">#></span> 7 1997-08-27 26 26.81148 2</span>
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<span class="r-out co"><span class="r-pr">#></span> 8 1978-01-26 46 46.39617 21</span>
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<span class="r-out co"><span class="r-pr">#></span> 9 1972-06-17 52 52.00546 27</span>
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<span class="r-out co"><span class="r-pr">#></span> 10 1986-08-10 37 37.85792 13</span>
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<span class="r-out co"><span class="r-pr">#></span> 1 1965-12-05 58 58.54918 34</span>
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<span class="r-out co"><span class="r-pr">#></span> 2 1980-03-01 44 44.31148 19</span>
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<span class="r-out co"><span class="r-pr">#></span> 3 1949-11-01 74 74.64208 50</span>
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<span class="r-out co"><span class="r-pr">#></span> 4 1947-02-14 77 77.35519 52</span>
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<span class="r-out co"><span class="r-pr">#></span> 5 1940-02-19 84 84.34153 59</span>
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<span class="r-out co"><span class="r-pr">#></span> 6 1988-01-10 36 36.45082 11</span>
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<span class="r-out co"><span class="r-pr">#></span> 7 1997-08-27 26 26.82240 2</span>
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<span class="r-out co"><span class="r-pr">#></span> 8 1978-01-26 46 46.40710 21</span>
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<span class="r-out co"><span class="r-pr">#></span> 9 1972-06-17 52 52.01639 27</span>
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<span class="r-out co"><span class="r-pr">#></span> 10 1986-08-10 37 37.86885 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"><h2>On this page</h2>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 59 × 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> 2024-06-19 <span style="color: #949494;">13:45:15</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> 2024-06-19 <span style="color: #949494;">13:45:21</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> 2024-06-19 <span style="color: #949494;">13:45:21</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> 2024-06-19 <span style="color: #949494;">13:45:22</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> 2024-06-19 <span style="color: #949494;">13:45:22</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2024-06-19 <span style="color: #949494;">13:45:15</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> 2024-06-19 <span style="color: #949494;">13:45:21</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> 2024-06-19 <span style="color: #949494;">13:45:21</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> 2024-06-19 <span style="color: #949494;">13:45:22</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> 2024-06-19 <span style="color: #949494;">13:45:22</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> 2024-06-23 <span style="color: #949494;">14:22:38</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> 2024-06-23 <span style="color: #949494;">14:22:44</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> 2024-06-23 <span style="color: #949494;">14:22:44</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> 2024-06-23 <span style="color: #949494;">14:22:44</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> 2024-06-23 <span style="color: #949494;">14:22:45</span> 4 genta Escheri… cattle GEN B_[ORD]_ENTRBCTR</span>
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<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2024-06-23 <span style="color: #949494;">14:22:38</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> 2024-06-23 <span style="color: #949494;">14:22:44</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> 2024-06-23 <span style="color: #949494;">14:22:44</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> 2024-06-23 <span style="color: #949494;">14:22:44</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> 2024-06-23 <span style="color: #949494;">14:22:45</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 49 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 11 Dec 2022. 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\") #> ClaLine 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 11 Dec 2022. 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\") #> ClaLine truncated
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Reference in new issue
Block a user