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articles/AMR.html
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website update since they are based on randomly created values and the
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a>. However, the methodology remains unchanged. This page was
generated on 27 February 2025.</p>
generated on 28 February 2025.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@@ -146,21 +146,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2025-02-27</td>
<td align="center">2025-02-28</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2025-02-27</td>
<td align="center">2025-02-28</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2025-02-27</td>
<td align="center">2025-02-28</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
articles/datasets.html
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<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">27 February 2025</h4>
<h4 data-toc-skip class="date">28 February 2025</h4>
<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>
<div class="d-none name"><code>datasets.Rmd</code></div>
pkgdown.yml
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@@ -12,7 +12,7 @@ articles:
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2025-02-27T15:47Z
last_built: 2025-02-28T07:24Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles
reference/age.html
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<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.23014 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.99452 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.32329 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 78 78.03562 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 85 85.02192 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 37 37.13151 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.50411 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 47 47.08767 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.69863 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.55068 13</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.23288 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.99726 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.32603 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 78 78.03836 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 85 85.02466 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 37 37.13425 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.50685 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 47 47.09041 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.70137 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.55342 13</span>
</code></pre></div>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
reference/as.sir.html
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<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 57 × 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: #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> 2025-02-27 <span style="color: #949494;">15:48:26</span> 4 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-02-27 <span style="color: #949494;">15:48:33</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-02-27 <span style="color: #949494;">15:48:33</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-02-27 <span style="color: #949494;">15:48:34</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-02-27 <span style="color: #949494;">15:48:34</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-02-27 <span style="color: #949494;">15:48:26</span> 3 AMX B_STRPT… human AMX <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-02-27 <span style="color: #949494;">15:48:33</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-02-27 <span style="color: #949494;">15:48:33</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-02-27 <span style="color: #949494;">15:48:34</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-02-27 <span style="color: #949494;">15:48:34</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 4 genta Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-02-28 <span style="color: #949494;">07:25:04</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-02-28 <span style="color: #949494;">07:25:04</span> 4 genta Escheri… cattle GEN <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 3 tobra Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-02-28 <span style="color: #949494;">07:25:04</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-02-28 <span style="color: #949494;">07:25:04</span> 3 tobra Escheri… horses TOB <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 2 cipro Escheri… human CIP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-02-28 <span style="color: #949494;">07:25:03</span> 2 cipro Escheri… human CIP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 47 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;chr&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>
search.json
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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