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</div>
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<div class="section level2">
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<h2 class="pkg-version" data-toc-text="2.1.1.9212" id="amr-2119212">AMR 2.1.1.9212<a class="anchor" aria-label="anchor" href="#amr-2119212"></a></h2>
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<h2 class="pkg-version" data-toc-text="2.1.1.9213" id="amr-2119213">AMR 2.1.1.9213<a class="anchor" aria-label="anchor" href="#amr-2119213"></a></h2>
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<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
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<div class="section level5">
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<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9212">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9212"></a></h5>
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<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9213">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9213"></a></h5>
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<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
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</div>
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<div class="section level3">
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<h3 id="breaking-2-1-1-9212">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9212"></a></h3>
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<h3 id="breaking-2-1-1-9213">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9213"></a></h3>
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<ul><li>Dataset <code>antibiotics</code> has been renamed to <code>antimicrobials</code> as the data set contains more than just antibiotics. Using <code>antibiotics</code> will still work, but now returns a warning.</li>
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<li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over two years ago.</li>
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</ul></div>
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<div class="section level3">
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<h3 id="new-2-1-1-9212">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9212"></a></h3>
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<h3 id="new-2-1-1-9213">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9213"></a></h3>
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<ul><li>
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<strong>One Health implementation</strong>
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<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
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</ul></li>
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</ul></div>
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<h3 id="changed-2-1-1-9212">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9212"></a></h3>
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<h3 id="changed-2-1-1-9213">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9213"></a></h3>
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<ul><li>SIR interpretation
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<ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
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<li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
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<li>Added console colours support of <code>sir</code> class for Positron</li>
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</ul></div>
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<div class="section level3">
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<h3 id="other-2-1-1-9212">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9212"></a></h3>
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<h3 id="other-2-1-1-9213">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9213"></a></h3>
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<ul><li>Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way</li>
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<li>Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations</li>
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<li>Greatly improved <code>vctrs</code> integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html" class="external-link">rowwise()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/c_across.html" class="external-link">c_across()</a></code> are now supported for e.g. columns of class <code>mic</code>. Despite this, this <code>AMR</code> package is still zero-dependent on any other package, including <code>dplyr</code> and <code>vctrs</code>.</li>
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<li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
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</ul></div>
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<div class="section level3">
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<h3 id="older-versions-2-1-1-9212">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9212"></a></h3>
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<h3 id="older-versions-2-1-1-9213">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9213"></a></h3>
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<p>This changelog only contains changes from AMR v3.0 (March 2025) and later.</p>
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<ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
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<li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>
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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: 2025-03-16T12:29Z
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last_built: 2025-03-16T12:44Z
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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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+10
-10
@@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
|
||||
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@@ -816,16 +816,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># A tibble: 65 × 16</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> datetime index ab_given mo_given host_given ab mo </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;"><ab></span> <span style="color: #949494; font-style: italic;"><mo></span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> 2025-03-16 <span style="color: #949494;">12:30:21</span> 1 ampicillin Strep p… human AMP <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> 2025-03-16 <span style="color: #949494;">12:30:21</span> 1 AMP Escheri… human AMP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> 2025-03-16 <span style="color: #949494;">12:30:21</span> 1 AMP Escheri… human AMP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 1</span> 2025-03-16 <span style="color: #949494;">12:45:17</span> 1 ampicillin Strep p… human AMP <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 2</span> 2025-03-16 <span style="color: #949494;">12:45:17</span> 1 AMP Escheri… human AMP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 3</span> 2025-03-16 <span style="color: #949494;">12:45:17</span> 1 AMP Escheri… human AMP <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 4</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;"> NA</span> </span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 5</span> 2025-03-16 <span style="color: #949494;">12:30:22</span> 1 GEN Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2025-03-16 <span style="color: #949494;">12:30:22</span> 1 TOB Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> 2025-03-16 <span style="color: #949494;">12:30:23</span> 1 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">#></span> <span style="color: #BCBCBC;"> 8</span> 2025-03-16 <span style="color: #949494;">12:30:23</span> 1 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">#></span> <span style="color: #BCBCBC;"> 9</span> 2025-03-16 <span style="color: #949494;">12:30:23</span> 2 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">#></span> <span style="color: #BCBCBC;">10</span> 2025-03-16 <span style="color: #949494;">12:30:23</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">#></span> <span style="color: #BCBCBC;"> 5</span> 2025-03-16 <span style="color: #949494;">12:45:18</span> 1 GEN Escheri… human GEN <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 6</span> 2025-03-16 <span style="color: #949494;">12:45:18</span> 1 TOB Escheri… human TOB <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBC…</span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #BCBCBC;"> 7</span> 2025-03-16 <span style="color: #949494;">12:45:18</span> 1 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">#></span> <span style="color: #BCBCBC;"> 8</span> 2025-03-16 <span style="color: #949494;">12:45:19</span> 1 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">#></span> <span style="color: #BCBCBC;"> 9</span> 2025-03-16 <span style="color: #949494;">12:45:19</span> 2 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">#></span> <span style="color: #BCBCBC;">10</span> 2025-03-16 <span style="color: #949494;">12:45:19</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">#></span> <span style="color: #949494;"># ℹ 55 more rows</span></span>
|
||||
<span class="r-out co"><span class="r-pr">#></span> <span style="color: #949494;"># ℹ 9 more variables: host <chr>, method <chr>, input <chr>, outcome <sir>,</span></span>
|
||||
<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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|
||||
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|
||||
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||||
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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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[{"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
|
||||
Reference in new issue
Block a user