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<a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -73,7 +73,7 @@
<span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span> <span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
<span></span> <span></span>
<span><span class="co"># Code coverage report</span></span> <span><span class="co"># Code coverage report</span></span>
<span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> <span><span class="fu">covr</span><span class="fu">::</span><span class="fu">package_coverage</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<p>From the shell:</p> <p>From the shell:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span> <div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div> <span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
@@ -92,13 +92,13 @@ _pkgdown.yml # pkgdown website configuration</code></pre>
<div class="section level2"> <div class="section level2">
<h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2> <h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
<p><strong>Naming conventions in <code>R/</code>:</strong></p> <p><strong>Naming conventions in <code>R/</code>:</strong></p>
<table class="table"><thead><tr class="header"><th>Prefix/Name</th> <table class="table"><thead><tr><th>Prefix/Name</th>
<th>Purpose</th> <th>Purpose</th>
</tr></thead><tbody><tr class="odd"><td><code>aa_*.R</code></td> </tr></thead><tbody><tr><td><code>aa_*.R</code></td>
<td>Loaded first (helpers, globals, options, package docs)</td> <td>Loaded first (helpers, globals, options, package docs)</td>
</tr><tr class="even"><td><code>zz_deprecated.R</code></td> </tr><tr><td><code>zz_deprecated.R</code></td>
<td>Deprecated function wrappers</td> <td>Deprecated function wrappers</td>
</tr><tr class="odd"><td><code>zzz.R</code></td> </tr><tr><td><code>zzz.R</code></td>
<td> <td>
<code>.onLoad</code> / <code>.onAttach</code> initialization</td> <code>.onLoad</code> / <code>.onAttach</code> initialization</td>
</tr></tbody></table><p><strong>Key source files:</strong></p> </tr></tbody></table><p><strong>Key source files:</strong></p>
@@ -136,46 +136,46 @@ _pkgdown.yml # pkgdown website configuration</code></pre>
<div class="section level2"> <div class="section level2">
<h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2> <h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
<p>The package defines five S3 classes with full print/format/plot/vctrs support:</p> <p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
<table class="table"><thead><tr class="header"><th>Class</th> <table class="table"><thead><tr><th>Class</th>
<th>Created by</th> <th>Created by</th>
<th>Represents</th> <th>Represents</th>
</tr></thead><tbody><tr class="odd"><td><code>&lt;mo&gt;</code></td> </tr></thead><tbody><tr><td><code>&lt;mo&gt;</code></td>
<td><code><a href="reference/as.mo.html">as.mo()</a></code></td> <td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
<td>Microorganism code</td> <td>Microorganism code</td>
</tr><tr class="even"><td><code>&lt;ab&gt;</code></td> </tr><tr><td><code>&lt;ab&gt;</code></td>
<td><code><a href="reference/as.ab.html">as.ab()</a></code></td> <td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
<td>Antimicrobial drug code</td> <td>Antimicrobial drug code</td>
</tr><tr class="odd"><td><code>&lt;av&gt;</code></td> </tr><tr><td><code>&lt;av&gt;</code></td>
<td><code><a href="reference/as.av.html">as.av()</a></code></td> <td><code><a href="reference/as.av.html">as.av()</a></code></td>
<td>Antiviral drug code</td> <td>Antiviral drug code</td>
</tr><tr class="even"><td><code>&lt;sir&gt;</code></td> </tr><tr><td><code>&lt;sir&gt;</code></td>
<td><code><a href="reference/as.sir.html">as.sir()</a></code></td> <td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
<td>SIR value (S/I/R/SDD)</td> <td>SIR value (S/I/R/SDD)</td>
</tr><tr class="odd"><td><code>&lt;mic&gt;</code></td> </tr><tr><td><code>&lt;mic&gt;</code></td>
<td><code><a href="reference/as.mic.html">as.mic()</a></code></td> <td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
<td>Minimum inhibitory concentration</td> <td>Minimum inhibitory concentration</td>
</tr><tr class="even"><td><code>&lt;disk&gt;</code></td> </tr><tr><td><code>&lt;disk&gt;</code></td>
<td><code><a href="reference/as.disk.html">as.disk()</a></code></td> <td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
<td>Disk diffusion diameter</td> <td>Disk diffusion diameter</td>
</tr></tbody></table></div> </tr></tbody></table></div>
<div class="section level2"> <div class="section level2">
<h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2> <h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
<p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p> <p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
<table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr class="header"><th>File</th> <table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
<th>Contents</th> <th>Contents</th>
</tr></thead><tbody><tr class="odd"><td><code>microorganisms.rda</code></td> </tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
<td>~79,000 microbial species with full taxonomy</td> <td>~79,000 microbial species with full taxonomy</td>
</tr><tr class="even"><td><code>antimicrobials.rda</code></td> </tr><tr><td><code>antimicrobials.rda</code></td>
<td>~620 antimicrobial drugs with ATC codes</td> <td>~620 antimicrobial drugs with ATC codes</td>
</tr><tr class="odd"><td><code>antivirals.rda</code></td> </tr><tr><td><code>antivirals.rda</code></td>
<td>Antiviral drugs</td> <td>Antiviral drugs</td>
</tr><tr class="even"><td><code>clinical_breakpoints.rda</code></td> </tr><tr><td><code>clinical_breakpoints.rda</code></td>
<td>EUCAST + CLSI breakpoints (2011–2025)</td> <td>EUCAST + CLSI breakpoints (2011–2025)</td>
</tr><tr class="odd"><td><code>intrinsic_resistant.rda</code></td> </tr><tr><td><code>intrinsic_resistant.rda</code></td>
<td>Intrinsic resistance patterns</td> <td>Intrinsic resistance patterns</td>
</tr><tr class="even"><td><code>example_isolates.rda</code></td> </tr><tr><td><code>example_isolates.rda</code></td>
<td>Example AMR dataset for documentation/testing</td> <td>Example AMR dataset for documentation/testing</td>
</tr><tr class="odd"><td><code>WHONET.rda</code></td> </tr><tr><td><code>WHONET.rda</code></td>
<td>Example WHONET-format dataset</td> <td>Example WHONET-format dataset</td>
</tr></tbody></table></div> </tr></tbody></table></div>
<div class="section level2"> <div class="section level2">

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@@ -23,6 +23,7 @@ Concentration (MIC) and disk diffusion handling - Multilingual output
All commands run inside an R session: All commands run inside an R session:
``` r ``` r
# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE) # Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
devtools::document() devtools::document()
@@ -94,14 +95,14 @@ R CMD check AMR
The package defines five S3 classes with full print/format/plot/vctrs The package defines five S3 classes with full print/format/plot/vctrs
support: support:
| Class | Created by | Represents | | Class | Created by | Represents |
|----------|-----------------------------------------------------------|----------------------------------| |----|----|----|
| `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code | | `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
| `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code | | `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
| `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code | | `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
| `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) | | `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
| `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration | | `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
| `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter | | `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
## Data Files ## Data Files
@@ -125,6 +126,7 @@ integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
are listed in `Suggests` and guarded with: are listed in `Suggests` and guarded with:
``` r ``` r
if (requireNamespace("pkg", quietly = TRUE)) { ... } if (requireNamespace("pkg", quietly = TRUE)) { ... }
``` ```

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<a class="navbar-brand me-2" href="index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -91,7 +91,7 @@
website update since they are based on randomly created values and the 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 page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a>. However, the methodology remains unchanged. This page was Markdown</a>. However, the methodology remains unchanged. This page was
generated on 25 April 2026.</p> generated on 30 April 2026.</p>
<div class="section level2"> <div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2> </h2>
@@ -147,21 +147,21 @@ make the structure of your data generally look like this:</p>
</tr></thead> </tr></thead>
<tbody> <tbody>
<tr class="odd"> <tr class="odd">
<td align="center">2026-04-25</td> <td align="center">2026-04-30</td>
<td align="center">abcd</td> <td align="center">abcd</td>
<td align="center">Escherichia coli</td> <td align="center">Escherichia coli</td>
<td align="center">S</td> <td align="center">S</td>
<td align="center">S</td> <td align="center">S</td>
</tr> </tr>
<tr class="even"> <tr class="even">
<td align="center">2026-04-25</td> <td align="center">2026-04-30</td>
<td align="center">abcd</td> <td align="center">abcd</td>
<td align="center">Escherichia coli</td> <td align="center">Escherichia coli</td>
<td align="center">S</td> <td align="center">S</td>
<td align="center">R</td> <td align="center">R</td>
</tr> </tr>
<tr class="odd"> <tr class="odd">
<td align="center">2026-04-25</td> <td align="center">2026-04-30</td>
<td align="center">efgh</td> <td align="center">efgh</td>
<td align="center">Escherichia coli</td> <td align="center">Escherichia coli</td>
<td align="center">R</td> <td align="center">R</td>

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@@ -3,7 +3,7 @@
**Note:** values on this page will change with every website update **Note:** values on this page will change with every website update
since they are based on randomly created values and the page was written since they are based on randomly created values and the page was written
in [R Markdown](https://rmarkdown.rstudio.com/). However, the in [R Markdown](https://rmarkdown.rstudio.com/). However, the
methodology remains unchanged. This page was generated on 25 April 2026. methodology remains unchanged. This page was generated on 30 April 2026.
## Introduction ## Introduction
@@ -51,9 +51,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP | | date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:| |:----------:|:----------:|:----------------:|:---:|:---:|
| 2026-04-25 | abcd | Escherichia coli | S | S | | 2026-04-30 | abcd | Escherichia coli | S | S |
| 2026-04-25 | abcd | Escherichia coli | S | R | | 2026-04-30 | abcd | Escherichia coli | S | R |
| 2026-04-25 | efgh | Escherichia coli | R | S | | 2026-04-30 | efgh | Escherichia coli | R | S |
### Needed R packages ### Needed R packages
@@ -69,6 +69,7 @@ We will also use the `cleaner` package, that can be used for cleaning
data and creating frequency tables. data and creating frequency tables.
``` r ``` r
library(dplyr) library(dplyr)
library(ggplot2) library(ggplot2)
library(AMR) library(AMR)
@@ -81,6 +82,7 @@ The `AMR` package contains a data set `example_isolates_unclean`, which
might look data that users have extracted from their laboratory systems: might look data that users have extracted from their laboratory systems:
``` r ``` r
example_isolates_unclean example_isolates_unclean
#> # A tibble: 3,000 × 8 #> # A tibble: 3,000 × 8
#> patient_id hospital date bacteria AMX AMC CIP GEN #> patient_id hospital date bacteria AMX AMC CIP GEN
@@ -119,6 +121,7 @@ still human readable. More importantly,
of input: of input:
``` r ``` r
as.mo("Klebsiella pneumoniae") as.mo("Klebsiella pneumoniae")
#> Class <mo> #> Class <mo>
#> [1] B_KLBSL_PNMN #> [1] B_KLBSL_PNMN
@@ -143,6 +146,7 @@ Gram-stain. They all start with `mo_` and they use
that still any arbitrary user input can be used: that still any arbitrary user input can be used:
``` r ``` r
mo_family("K. pneumoniae") mo_family("K. pneumoniae")
#> [1] "Enterobacteriaceae" #> [1] "Enterobacteriaceae"
mo_genus("K. pneumoniae") mo_genus("K. pneumoniae")
@@ -165,6 +169,7 @@ mo_snomed("K. pneumoniae")
Now we can thus clean our data: Now we can thus clean our data:
``` r ``` r
our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL", #> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
#> "KLEPNE", "STAAUR", and "STRPNE". #> "KLEPNE", "STAAUR", and "STRPNE".
@@ -177,6 +182,7 @@ Apparently, there was some uncertainty about the translation to
taxonomic codes. Let’s check this: taxonomic codes. Let’s check this:
``` r ``` r
mo_uncertainties() mo_uncertainties()
#> Matching scores are based on the resemblance between the input and the full #> Matching scores are based on the resemblance between the input and the full
#> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`. #> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.
@@ -231,6 +237,7 @@ diffusion values, read more about that on the
For now, we will just clean the SIR columns in our data using dplyr: For now, we will just clean the SIR columns in our data using dplyr:
``` r ``` r
# method 1, be explicit about the columns: # method 1, be explicit about the columns:
our_data <- our_data %>% our_data <- our_data %>%
mutate_at(vars(AMX:GEN), as.sir) mutate_at(vars(AMX:GEN), as.sir)
@@ -308,6 +315,7 @@ page.
The outcome of the function can easily be added to our data: The outcome of the function can easily be added to our data:
``` r ``` r
our_data <- our_data %>% our_data <- our_data %>%
mutate(first = first_isolate(info = TRUE)) mutate(first = first_isolate(info = TRUE))
#> ℹ Determining first isolates using an episode length of 365 days #> ℹ Determining first isolates using an episode length of 365 days
@@ -326,6 +334,7 @@ with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
function, also from the `dplyr` package: function, also from the `dplyr` package:
``` r ``` r
our_data_1st <- our_data %>% our_data_1st <- our_data %>%
filter(first == TRUE) filter(first == TRUE)
``` ```
@@ -333,6 +342,7 @@ our_data_1st <- our_data %>%
For future use, the above two syntaxes can be shortened: For future use, the above two syntaxes can be shortened:
``` r ``` r
our_data_1st <- our_data %>% our_data_1st <- our_data %>%
filter_first_isolate() filter_first_isolate()
``` ```
@@ -340,6 +350,7 @@ our_data_1st <- our_data %>%
So we end up with 2 724 isolates for analysis. Now our data looks like: So we end up with 2 724 isolates for analysis. Now our data looks like:
``` r ``` r
our_data_1st our_data_1st
#> # A tibble: 2,724 × 9 #> # A tibble: 2,724 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first #> patient_id hospital date bacteria AMX AMC CIP GEN first
@@ -366,6 +377,7 @@ gives a good first impression, as it comes with support for the new `mo`
and `sir` classes that we now have in our data set: and `sir` classes that we now have in our data set:
``` r ``` r
summary(our_data_1st) summary(our_data_1st)
#> patient_id hospital date bacteria #> patient_id hospital date bacteria
#> Length :2724 Length :2724 Min. :2011-01-01 Class :mo #> Length :2724 Length :2724 Min. :2011-01-01 Class :mo
@@ -417,6 +429,7 @@ table with [`count()`](https://amr-for-r.org/reference/count.md) based
on the name of the microorganisms: on the name of the microorganisms:
``` r ``` r
our_data %>% our_data %>%
count(mo_name(bacteria), sort = TRUE) count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2 #> # A tibble: 4 × 2
@@ -445,6 +458,7 @@ columns based on the antibiotic class that your antibiotic results are
in: in:
``` r ``` r
our_data_1st %>% our_data_1st %>%
select(date, aminoglycosides()) select(date, aminoglycosides())
#> ℹ For `aminoglycosides()` using column GEN #> ℹ For `aminoglycosides()` using column GEN
@@ -590,6 +604,7 @@ function to create any of the above antibiogram types. For starters,
this is what the included `example_isolates` data set looks like: this is what the included `example_isolates` data set looks like:
``` r ``` r
example_isolates example_isolates
#> # A tibble: 2,000 × 46 #> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX #> date patient age gender ward mo PEN OXA FLC AMX
@@ -622,6 +637,7 @@ function supports any (combination) of the previously mentioned
antibiotic class selectors: antibiotic class selectors:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()) antibiotics = c(aminoglycosides(), carbapenems())
) )
@@ -630,18 +646,18 @@ antibiogram(example_isolates,
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
``` ```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------| |:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | | CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | | *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) | | *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) | | *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) | | *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) | | *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) | | *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) | | *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) | | *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | | *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
Notice that the Notice that the
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md) [`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
@@ -659,6 +675,7 @@ Ukrainian, Urdu, or Vietnamese. In this next example, we force the
language to be Spanish using the `language` argument: language to be Spanish using the `language` argument:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
mo_transform = "gramstain", mo_transform = "gramstain",
antibiotics = aminoglycosides(), antibiotics = aminoglycosides(),
@@ -669,10 +686,10 @@ antibiogram(example_isolates,
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
``` ```
| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina | | Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
|:--------------|:-------------------|:--------------------|:----------------|:-------------------| |:---|:---|:---|:---|:---|
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) | | Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) | | Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
#### Combined Antibiogram #### Combined Antibiogram
@@ -680,6 +697,7 @@ To create a combined antibiogram, use antibiotic codes or names with a
plus `+` character like this: plus `+` character like this:
``` r ``` r
combined_ab <- antibiogram(example_isolates, combined_ab <- antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
ab_transform = NULL ab_transform = NULL
@@ -687,17 +705,17 @@ combined_ab <- antibiogram(example_isolates,
combined_ab combined_ab
``` ```
| Pathogen | TZP | TZP + GEN | TZP + TOB | | Pathogen | TZP | TZP + GEN | TZP + TOB |
|:-----------------|:---------------------|:---------------------|:---------------------| |:---|:---|:---|:---|
| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA | | CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) | | *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) | | *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) | | *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) | | *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) | | *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) | | *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) | | *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) | | *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |
#### Syndromic Antibiogram #### Syndromic Antibiogram
@@ -707,6 +725,7 @@ be used. This can be any column in the data, or e.g. an
on certain columns: on certain columns:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()), antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward" syndromic_group = "ward"
@@ -716,22 +735,22 @@ antibiogram(example_isolates,
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
``` ```
| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | | Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:----------------|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------| |:---|:---|:---|:---|:---|:---|:---|:---|
| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) | | Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA | | ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA | | Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) | | Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) | | ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) | | Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) | | Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) | | Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA | | ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) | | Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) | | ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) | | Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | | Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | | ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
#### Weighted-Incidence Syndromic Combination Antibiogram (WISCA) #### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
@@ -745,6 +764,7 @@ susceptibility estimates, weighted by pathogen incidence and
antimicrobial susceptibility patterns. antimicrobial susceptibility patterns.
``` r ``` r
example_isolates %>% example_isolates %>%
wisca( wisca(
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
@@ -753,8 +773,8 @@ example_isolates %>%
``` ```
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:------------------------|:-------------------------------------|:-------------------------------------| |:---|:---|:---|
| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) | | 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
WISCA uses a **Bayesian decision model** to integrate data from multiple WISCA uses a **Bayesian decision model** to integrate data from multiple
pathogens, improving empirical therapy guidance, especially for pathogens, improving empirical therapy guidance, especially for
@@ -775,6 +795,7 @@ grouped `tibble`, i.e., using
first: first:
``` r ``` r
example_isolates %>% example_isolates %>%
top_n_microorganisms(n = 10) %>% top_n_microorganisms(n = 10) %>%
group_by( group_by(
@@ -785,15 +806,15 @@ example_isolates %>%
``` ```
| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:----------|:-------|:------------------------|:-------------------------------------|:-------------------------------------| |:---|:---|:---|:---|:---|
| 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) | | 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) |
| 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) | | 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) |
| 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) | | 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) |
| 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) | | 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) |
| 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) | | 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) |
| 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) | | 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) |
| 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) | | 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) |
| 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) | | 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) |
#### Plotting antibiograms #### Plotting antibiograms
@@ -803,6 +824,7 @@ from the `ggplot2` packages, since this `AMR` package provides an
extension to that function: extension to that function:
``` r ``` r
autoplot(combined_ab) autoplot(combined_ab)
``` ```
@@ -847,6 +869,7 @@ equal to
These functions can be used on their own: These functions can be used on their own:
``` r ``` r
our_data_1st %>% resistance(AMX) our_data_1st %>% resistance(AMX)
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> category susceptible. Set the `guideline` argument or the `AMR_guideline`
@@ -861,6 +884,7 @@ Or can be used in conjunction with
both from the `dplyr` package: both from the `dplyr` package:
``` r ``` r
our_data_1st %>% our_data_1st %>%
group_by(hospital) %>% group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX)) summarise(amoxicillin = resistance(AMX))
@@ -881,6 +905,7 @@ example with randomly generated MIC values for *Klebsiella pneumoniae*
and ciprofloxacin: and ciprofloxacin:
``` r ``` r
set.seed(123) set.seed(123)
mic_values <- random_mic(100) mic_values <- random_mic(100)
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024") sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
@@ -916,6 +941,7 @@ y-axis and
colour-code SIR categories. colour-code SIR categories.
``` r ``` r
# add a group # add a group
my_data$group <- rep(c("A", "B", "C", "D"), each = 25) my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
@@ -946,6 +972,7 @@ has been extended by this package to directly plot MIC and disk
diffusion values: diffusion values:
``` r ``` r
autoplot(mic_values) autoplot(mic_values)
``` ```
@@ -953,6 +980,7 @@ autoplot(mic_values)
``` r ``` r
# by providing `mo` and `ab`, colours will indicate the SIR interpretation: # by providing `mo` and `ab`, colours will indicate the SIR interpretation:
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024") autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
``` ```

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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

52
articles/AMR_for_Python.md
View File

@@ -168,37 +168,37 @@ regular Python data frames:
AMR.microorganisms AMR.microorganisms
``` ```
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence | | mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|--------------|------------------------------------|----------|----------|----------|-------------|-----------------|------------| |----|----|----|----|----|----|----|----|
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 | | B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 | | B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 | | B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
| … | … | … | … | … | … | … | … | | … | … | … | … | … | … | … | … |
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 | | B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 | | B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 | | B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 | | B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 | | F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
``` python ``` python
AMR.antimicrobials AMR.antimicrobials
``` ```
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units | | ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|-----|------------|-----------------------|--------------------------|----------|------------|--------|----------| |----|----|----|----|----|----|----|----|
| AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None | | AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None | | ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None | | ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None | | ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g | | AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
| … | … | … | … | … | … | … | … | | … | … | … | … | … | … | … | … |
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None | | VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g | | VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None | | XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None | | ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None | | ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
## Conclusion ## Conclusion

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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

23
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@@ -49,6 +49,7 @@ We begin by loading the required libraries and preparing the
`example_isolates` dataset from the `AMR` package. `example_isolates` dataset from the `AMR` package.
``` r ``` r
# Load required libraries # Load required libraries
library(AMR) # For AMR data analysis library(AMR) # For AMR data analysis
library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
@@ -57,6 +58,7 @@ library(tidymodels) # For machine learning workflows, and data manipulation (dpl
Prepare the data: Prepare the data:
``` r ``` r
# Your data could look like this: # Your data could look like this:
example_isolates example_isolates
#> # A tibble: 2,000 × 46 #> # A tibble: 2,000 × 46
@@ -127,6 +129,7 @@ preprocessing, model specification, and fitting.
We create a recipe to preprocess the data for modelling. We create a recipe to preprocess the data for modelling.
``` r ``` r
# Define the recipe for data preprocessing # Define the recipe for data preprocessing
resistance_recipe <- recipe(mo ~ ., data = data) %>% resistance_recipe <- recipe(mo ~ ., data = data) %>%
step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
@@ -148,6 +151,7 @@ have with `step_corr()`, the necessary parameters can be estimated from
a training set using `prep()`: a training set using `prep()`:
``` r ``` r
prep(resistance_recipe) prep(resistance_recipe)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
@@ -201,6 +205,7 @@ We define a logistic regression model since resistance prediction is a
binary classification task. binary classification task.
``` r ``` r
# Specify a logistic regression model # Specify a logistic regression model
logistic_model <- logistic_reg() %>% logistic_model <- logistic_reg() %>%
set_engine("glm") # Use the Generalised Linear Model engine set_engine("glm") # Use the Generalised Linear Model engine
@@ -221,6 +226,7 @@ We bundle the recipe and model together into a `workflow`, which
organises the entire modelling process. organises the entire modelling process.
``` r ``` r
# Combine the recipe and model into a workflow # Combine the recipe and model into a workflow
resistance_workflow <- workflow() %>% resistance_workflow <- workflow() %>%
add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
@@ -248,6 +254,7 @@ Then, we fit the workflow on the training set and evaluate its
performance. performance.
``` r ``` r
# Split data into training and testing sets # Split data into training and testing sets
set.seed(123) # For reproducibility set.seed(123) # For reproducibility
data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
@@ -271,6 +278,7 @@ they are stored in the recipe.
Next, we evaluate the model on the testing data. Next, we evaluate the model on the testing data.
``` r ``` r
# Make predictions on the testing set # Make predictions on the testing set
predictions <- fitted_workflow %>% predictions <- fitted_workflow %>%
predict(testing_data) # Generate predictions predict(testing_data) # Generate predictions
@@ -338,6 +346,7 @@ AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
curve looks like this: curve looks like this:
``` r ``` r
predictions %>% predictions %>%
roc_curve(mo, `.pred_Gram-negative`) %>% roc_curve(mo, `.pred_Gram-negative`) %>%
autoplot() autoplot()
@@ -390,6 +399,7 @@ Our goal is to:
We use the `esbl_isolates` dataset that comes with the AMR package. We use the `esbl_isolates` dataset that comes with the AMR package.
``` r ``` r
# Load required libraries # Load required libraries
library(AMR) library(AMR)
library(tidymodels) library(tidymodels)
@@ -437,6 +447,7 @@ selected using the new
[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md): [`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
``` r ``` r
# Split into training and testing sets # Split into training and testing sets
set.seed(123) set.seed(123)
split <- initial_split(data) split <- initial_split(data)
@@ -480,6 +491,7 @@ manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html
could be much more precise. could be much more precise.
``` r ``` r
# Define the model # Define the model
model <- logistic_reg(mode = "classification") %>% model <- logistic_reg(mode = "classification") %>%
set_engine("glm") set_engine("glm")
@@ -498,6 +510,7 @@ model
#### 3. Building the Workflow #### 3. Building the Workflow
``` r ``` r
# Create workflow # Create workflow
workflow_model <- workflow() %>% workflow_model <- workflow() %>%
add_recipe(mic_recipe) %>% add_recipe(mic_recipe) %>%
@@ -522,6 +535,7 @@ workflow_model
### **Training and Evaluating the Model** ### **Training and Evaluating the Model**
``` r ``` r
# Fit the model # Fit the model
fitted <- fit(workflow_model, training_data) fitted <- fit(workflow_model, training_data)
@@ -566,6 +580,7 @@ We can visualise predictions by comparing predicted and actual ESBL
status. status.
``` r ``` r
library(ggplot2) library(ggplot2)
ggplot(predictions, aes(x = esbl, fill = .pred_class)) + ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
@@ -583,6 +598,7 @@ ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
And plot the certainties too - how certain were the actual predictions? And plot the certainties too - how certain were the actual predictions?
``` r ``` r
predictions %>% predictions %>%
mutate( mutate(
certainty = ifelse(.pred_class == "FALSE", certainty = ifelse(.pred_class == "FALSE",
@@ -646,6 +662,7 @@ We start by transforming the `example_isolates` dataset into a
structured time-series format. structured time-series format.
``` r ``` r
# Load required libraries # Load required libraries
library(AMR) library(AMR)
library(tidymodels) library(tidymodels)
@@ -706,6 +723,7 @@ step, a model specification, and the fitting process.
#### 1. Preprocessing with a Recipe #### 1. Preprocessing with a Recipe
``` r ``` r
# Define the recipe # Define the recipe
resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>% resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
@@ -739,6 +757,7 @@ resistance_recipe_time
We use a linear regression model to predict resistance trends. We use a linear regression model to predict resistance trends.
``` r ``` r
# Define the linear regression model # Define the linear regression model
lm_model <- linear_reg() %>% lm_model <- linear_reg() %>%
set_engine("lm") # Use linear regression set_engine("lm") # Use linear regression
@@ -759,6 +778,7 @@ lm_model
We combine the preprocessing recipe and model into a workflow. We combine the preprocessing recipe and model into a workflow.
``` r ``` r
# Create workflow # Create workflow
resistance_workflow_time <- workflow() %>% resistance_workflow_time <- workflow() %>%
add_recipe(resistance_recipe_time) %>% add_recipe(resistance_recipe_time) %>%
@@ -788,6 +808,7 @@ We split the data into training and testing sets, fit the model, and
evaluate performance. evaluate performance.
``` r ``` r
# Split the data # Split the data
set.seed(123) set.seed(123)
data_split_time <- initial_split(data_time, prop = 0.8) data_split_time <- initial_split(data_time, prop = 0.8)
@@ -829,6 +850,7 @@ metrics_time
We plot resistance trends over time for amoxicillin. We plot resistance trends over time for amoxicillin.
``` r ``` r
library(ggplot2) library(ggplot2)
# Plot actual vs predicted resistance over time # Plot actual vs predicted resistance over time
@@ -849,6 +871,7 @@ Additionally, we can visualise resistance trends in `ggplot2` and
directly add linear models there: directly add linear models there:
``` r ``` r
ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) + ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
geom_line() + geom_line() +
labs( labs(

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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

5
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@@ -36,6 +36,7 @@ function resolves this, by applying the latest ‘EUCAST Expected
Resistant Phenotypes’ guideline: Resistant Phenotypes’ guideline:
``` r ``` r
oops <- tibble::tibble( oops <- tibble::tibble(
mo = c( mo = c(
"Klebsiella pneumoniae", "Klebsiella pneumoniae",
@@ -64,6 +65,7 @@ that uses the same guideline, but allows to check for one or more
specific microorganisms or antimicrobials: specific microorganisms or antimicrobials:
``` r ``` r
mo_is_intrinsic_resistant( mo_is_intrinsic_resistant(
c("Klebsiella pneumoniae", "Escherichia coli"), c("Klebsiella pneumoniae", "Escherichia coli"),
"ampicillin" "ampicillin"
@@ -83,6 +85,7 @@ other antimicrobials drugs. This process is called *interpretive
reading*, and is basically a form of imputation: reading*, and is basically a form of imputation:
``` r ``` r
data <- tibble::tibble( data <- tibble::tibble(
mo = c( mo = c(
"Staphylococcus aureus", "Staphylococcus aureus",
@@ -102,6 +105,7 @@ data <- tibble::tibble(
``` ```
``` r ``` r
data data
``` ```
@@ -114,6 +118,7 @@ data
| Pseudomonas aeruginosa | \- | \- | \- | \- | \- | S | S | | Pseudomonas aeruginosa | \- | \- | \- | \- | \- | S | S |
``` r ``` r
eucast_rules(data, overwrite = TRUE) eucast_rules(data, overwrite = TRUE)
``` ```

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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

7
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@@ -11,6 +11,7 @@ For PCA, we need to transform our AMR data first. This is what the
`example_isolates` data set in this package looks like: `example_isolates` data set in this package looks like:
``` r ``` r
library(AMR) library(AMR)
library(dplyr) library(dplyr)
glimpse(example_isolates) glimpse(example_isolates)
@@ -68,6 +69,7 @@ Now to transform this to a data set with only resistance percentages per
taxonomic order and genus: taxonomic order and genus:
``` r ``` r
resistance_data <- example_isolates %>% resistance_data <- example_isolates %>%
group_by( group_by(
order = mo_order(mo), # group on anything, like order order = mo_order(mo), # group on anything, like order
@@ -103,6 +105,7 @@ automatically filter on rows that contain numeric values in all selected
variables, so we now only need to do: variables, so we now only need to do:
``` r ``` r
pca_result <- pca(resistance_data) pca_result <- pca(resistance_data)
#> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m", #> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
#> "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m", #> "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
@@ -114,6 +117,7 @@ The result can be reviewed with the good old
[`summary()`](https://rdrr.io/r/base/summary.html) function: [`summary()`](https://rdrr.io/r/base/summary.html) function:
``` r ``` r
summary(pca_result) summary(pca_result)
#> Groups (n=4, named as 'order'): #> Groups (n=4, named as 'order'):
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales" #> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
@@ -137,6 +141,7 @@ microorganism.
## Plotting the results ## Plotting the results
``` r ``` r
biplot(pca_result) biplot(pca_result)
``` ```
@@ -148,6 +153,7 @@ better with our new
function, that automatically adds the right labels and even groups: function, that automatically adds the right labels and even groups:
``` r ``` r
ggplot_pca(pca_result) ggplot_pca(pca_result)
``` ```
@@ -156,6 +162,7 @@ ggplot_pca(pca_result)
You can also print an ellipse per group, and edit the appearance: You can also print an ellipse per group, and edit the appearance:
``` r ``` r
ggplot_pca(pca_result, ellipse = TRUE) + ggplot_pca(pca_result, ellipse = TRUE) +
ggplot2::labs(title = "An AMR/PCA biplot!") ggplot2::labs(title = "An AMR/PCA biplot!")
``` ```

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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

30
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@@ -11,6 +11,7 @@ date fields are imported correctly.
An example syntax could look like this: An example syntax could look like this:
``` r ``` r
library(readxl) library(readxl)
data <- read_excel(path = "path/to/your/file.xlsx") data <- read_excel(path = "path/to/your/file.xlsx")
``` ```
@@ -27,6 +28,7 @@ I suggest you read about it on their website:
<https://www.tidyverse.org/>. <https://www.tidyverse.org/>.
``` r ``` r
library(dplyr) # part of tidyverse library(dplyr) # part of tidyverse
library(ggplot2) # part of tidyverse library(ggplot2) # part of tidyverse
library(AMR) # this package library(AMR) # this package
@@ -50,6 +52,7 @@ analysis:
for. for.
``` r ``` r
# transform variables # transform variables
data <- WHONET %>% data <- WHONET %>%
# get microbial ID based on given organism # get microbial ID based on given organism
@@ -68,6 +71,7 @@ function can be used to create frequency tables.
So let’s check our data, with a couple of frequency tables: So let’s check our data, with a couple of frequency tables:
``` r ``` r
# our newly created `mo` variable, put in the mo_name() function # our newly created `mo` variable, put in the mo_name() function
data %>% freq(mo_name(mo), nmax = 10) data %>% freq(mo_name(mo), nmax = 10)
``` ```
@@ -82,22 +86,23 @@ Unique: 38
Shortest: 11 Shortest: 11
Longest: 40 Longest: 40
| | Item | Count | Percent | Cum. Count | Cum. Percent | | | Item | Count | Percent | Cum. Count | Cum. Percent |
|:----|:-----------------------------------------|------:|--------:|-----------:|-------------:| |:---|:---|---:|---:|---:|---:|
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% | | 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% | | 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% | | 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% | | 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% | | 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% | | 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% | | 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% | | 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% | | 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% | | 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
(omitted 28 entries, n = 57 \[11.4%\]) (omitted 28 entries, n = 57 \[11.4%\])
``` r ``` r
# our transformed antibiotic columns # our transformed antibiotic columns
# amoxicillin/clavulanic acid (J01CR02) as an example # amoxicillin/clavulanic acid (J01CR02) as an example
data %>% freq(AMC_ND2) data %>% freq(AMC_ND2)
@@ -132,6 +137,7 @@ included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
function: function:
``` r ``` r
data %>% data %>%
group_by(Country) %>% group_by(Country) %>%
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>% select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%

20
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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -151,7 +151,7 @@ isolates. Therefore, traditional antibiograms:</p>
regimen.</li> regimen.</li>
</ul> </ul>
<p>We can write this as:</p> <p>We can write this as:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mrow><mo stretchy="true" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p> <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
<p>For example, suppose:</p> <p>For example, suppose:</p>
<ul> <ul>
<li> <li>
@@ -162,7 +162,7 @@ are susceptible to a drug.</li>
<em>Klebsiella</em> are susceptible.</li> <em>Klebsiella</em> are susceptible.</li>
</ul> </ul>
<p>Then:</p> <p>Then:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p> <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
<p>But in real data, incidence and susceptibility are <strong>estimated <p>But in real data, incidence and susceptibility are <strong>estimated
from samples</strong>, so they carry uncertainty. WISCA models this from samples</strong>, so they carry uncertainty. WISCA models this
<strong>probabilistically</strong>, using conjugate Bayesian <strong>probabilistically</strong>, using conjugate Bayesian
@@ -180,16 +180,16 @@ distributions.</p>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
be the number of pathogens,</li> be the number of pathogens,</li>
<li> <li>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\alpha = (1, 1, \ldots, 1)</annotation></semantics></math>
be a <strong>Dirichlet</strong> prior (uniform),</li> be a <strong>Dirichlet</strong> prior (uniform),</li>
<li> <li>
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">n = (n_1, \ldots, n_K)</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">n = (n_1, \ldots, n_K)</annotation></semantics></math>
be the observed counts per species.</li> be the observed counts per species.</li>
</ul> </ul>
<p>Then the posterior incidence is:</p> <p>Then the posterior incidence is:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p> <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
<p>To simulate from this, we use:</p> <p>To simulate from this, we use:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p> <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
</div> </div>
<div class="section level3"> <div class="section level3">
<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a> <h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
@@ -197,7 +197,7 @@ be the observed counts per species.</li>
<p>Each pathogen–regimen pair has a prior and data:</p> <p>Each pathogen–regimen pair has a prior and data:</p>
<ul> <ul>
<li>Prior: <li>Prior:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>, <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>,</mo><msub><mi>β</mi><mn>0</mn></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(\alpha_0, \beta_0)</annotation></semantics></math>,
with default with default
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>α</mi><mn>0</mn></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\alpha_0 = \beta_0 = 1</annotation></semantics></math>
</li> </li>
@@ -213,7 +213,7 @@ category could also include values SDD (susceptible, dose-dependent) and
I (intermediate [CLSI], or susceptible, increased exposure I (intermediate [CLSI], or susceptible, increased exposure
[EUCAST]).</p> [EUCAST]).</p>
<p>Then the posterior is:</p> <p>Then the posterior is:</p>
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p> <p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
</div> </div>
<div class="section level3"> <div class="section level3">
<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a> <h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
@@ -224,7 +224,7 @@ I (intermediate [CLSI], or susceptible, increased exposure
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
</li> </li>
<li>Simulate susceptibility: <li>Simulate susceptibility:
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo>+</mo><msub><mi>R</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)</annotation></semantics></math>
</li> </li>
<li>Combine:</li> <li>Combine:</li>
</ol> </ol>

66
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@@ -58,7 +58,9 @@ This means combining two things:
We can write this as: We can write this as:
$$\text{Coverage} = \sum\limits_{i}\left( \text{Incidence}_{i} \times \text{Susceptibility}_{i} \right)$$ ``` math
\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
```
For example, suppose: For example, suppose:
@@ -69,7 +71,9 @@ For example, suppose:
Then: Then:
$$\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82$$ ``` math
\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
```
But in real data, incidence and susceptibility are **estimated from But in real data, incidence and susceptibility are **estimated from
samples**, so they carry uncertainty. WISCA models this samples**, so they carry uncertainty. WISCA models this
@@ -81,45 +85,53 @@ samples**, so they carry uncertainty. WISCA models this
Let: Let:
- $K$ be the number of pathogens, - $`K`$ be the number of pathogens,
- $\alpha = (1,1,\ldots,1)$ be a **Dirichlet** prior (uniform), - $`\alpha = (1, 1, \ldots, 1)`$ be a **Dirichlet** prior (uniform),
- $n = \left( n_{1},\ldots,n_{K} \right)$ be the observed counts per - $`n = (n_1, \ldots, n_K)`$ be the observed counts per species.
species.
Then the posterior incidence is: Then the posterior incidence is:
$$p \sim \text{Dirichlet}\left( \alpha_{1} + n_{1},\ldots,\alpha_{K} + n_{K} \right)$$ ``` math
p \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
```
To simulate from this, we use: To simulate from this, we use:
$$x_{i} \sim \text{Gamma}\left( \alpha_{i} + n_{i},\ 1 \right),\quad p_{i} = \frac{x_{i}}{\sum\limits_{j = 1}^{K}x_{j}}$$ ``` math
x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
```
### Susceptibility ### Susceptibility
Each pathogen–regimen pair has a prior and data: Each pathogen–regimen pair has a prior and data:
- Prior: $\text{Beta}\left( \alpha_{0},\beta_{0} \right)$, with default - Prior: $`\text{Beta}(\alpha_0, \beta_0)`$, with default
$\alpha_{0} = \beta_{0} = 1$ $`\alpha_0 = \beta_0 = 1`$
- Data: $S$ susceptible out of $N$ tested - Data: $`S`$ susceptible out of $`N`$ tested
The $S$ category could also include values SDD (susceptible, The $`S`$ category could also include values SDD (susceptible,
dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
exposure \[EUCAST\]). exposure \[EUCAST\]).
Then the posterior is: Then the posterior is:
$$\theta \sim \text{Beta}\left( \alpha_{0} + S,\ \beta_{0} + N - S \right)$$ ``` math
\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
```
### Final coverage estimate ### Final coverage estimate
Putting it together: Putting it together:
1. Simulate pathogen incidence: $\mathbf{p} \sim \text{Dirichlet}$ 1. Simulate pathogen incidence:
$`\boldsymbol{p} \sim \text{Dirichlet}`$
2. Simulate susceptibility: 2. Simulate susceptibility:
$\theta_{i} \sim \text{Beta}\left( 1 + S_{i},\ 1 + R_{i} \right)$ $`\theta_i \sim \text{Beta}(1 + S_i,\ 1 + R_i)`$
3. Combine: 3. Combine:
$$\text{Coverage} = \sum\limits_{i = 1}^{K}p_{i} \cdot \theta_{i}$$ ``` math
\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
```
Repeat this simulation (e.g. 1000×) and summarise: Repeat this simulation (e.g. 1000×) and summarise:
@@ -131,6 +143,7 @@ Repeat this simulation (e.g. 1000×) and summarise:
### Prepare data and simulate synthetic syndrome ### Prepare data and simulate synthetic syndrome
``` r ``` r
library(AMR) library(AMR)
data <- example_isolates data <- example_isolates
@@ -164,6 +177,7 @@ data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "No UTI")
### Basic WISCA antibiogram ### Basic WISCA antibiogram
``` r ``` r
wisca(data, wisca(data,
antimicrobials = c("AMC", "CIP", "GEN") antimicrobials = c("AMC", "CIP", "GEN")
) )
@@ -176,18 +190,20 @@ wisca(data,
### Use combination regimens ### Use combination regimens
``` r ``` r
wisca(data, wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN") antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
) )
``` ```
| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin | | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|:----------------------------|:--------------------------------------------|:-----------------------------------------| |:---|:---|:---|
| 73.8% (71.8-75.7%) | 87.5% (85.9-89%) | 89.7% (88.2-91.1%) | | 73.8% (71.8-75.7%) | 87.5% (85.9-89%) | 89.7% (88.2-91.1%) |
### Stratify by syndrome ### Stratify by syndrome
``` r ``` r
wisca(data, wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
syndromic_group = "syndrome" syndromic_group = "syndrome"
@@ -195,15 +211,16 @@ wisca(data,
``` ```
| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin | | Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|:----------------|:----------------------------|:--------------------------------------------|:-----------------------------------------| |:---|:---|:---|:---|
| No UTI | 70.1% (67.8-72.3%) | 85.2% (83.1-87.2%) | 87.1% (85.3-88.7%) | | No UTI | 70.1% (67.8-72.3%) | 85.2% (83.1-87.2%) | 87.1% (85.3-88.7%) |
| UTI | 80.9% (77.7-83.8%) | 88.2% (85.7-90.5%) | 90.9% (88.7-93%) | | UTI | 80.9% (77.7-83.8%) | 88.2% (85.7-90.5%) | 90.9% (88.7-93%) |
The `AMR` package is available in 28 languages, which can all be used The `AMR` package is available in 28 languages, which can all be used
for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md) for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
function too: function too:
``` r ``` r
wisca(data, wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
syndromic_group = gsub("UTI", "UCI", data$syndrome), syndromic_group = gsub("UTI", "UCI", data$syndrome),
@@ -212,9 +229,9 @@ wisca(data,
``` ```
| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina | | Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
|:-----------------|:------------------------------|:-----------------------------------------------|:--------------------------------------------| |:---|:---|:---|:---|
| No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) | | No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) |
| UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) | | UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) |
## Sensible defaults, which can be customised ## Sensible defaults, which can be customised
@@ -242,6 +259,7 @@ WISCA enables:
It is available in the `AMR` package via either: It is available in the `AMR` package via either:
``` r ``` r
wisca(...) wisca(...)
antibiogram(..., wisca = TRUE) antibiogram(..., wisca = TRUE)

4
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@@ -30,7 +30,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -80,7 +80,7 @@
<main id="main" class="col-md-9"><div class="page-header"> <main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1> <img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
<h4 data-toc-skip class="date">25 April 2026</h4> <h4 data-toc-skip class="date">30 April 2026</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> <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> <div class="d-none name"><code>datasets.Rmd</code></div>

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@@ -78,14 +78,14 @@ Included (sub)species per taxonomic kingdom:
First 6 rows when filtering on genus *Escherichia*: First 6 rows when filtering on genus *Escherichia*:
| mo | fullname | status | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed | | mo | fullname | status | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
|:-----------------:|:--------------------------:|:--------:|:--------:|:--------------:|:-------------------:|:----------------:|:------------------:|:-----------:|:--------------:|:----------:|:----------:|:-----------------------:|:---------------------------:|:------:|:------:|:-----------:|:---------------:|:--------:|:---------------:|:-------------------:|:--------:|:-----------:|:---------------:|:----------:|:-----------------------------------------:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | LPSN | 515602 | 482 | | | | | | 11158430 | | 1 | 407310004, 407251000, 407281008, … | | B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | LPSN | 515602 | 482 | | | | | | 11158430 | | 1 | 407310004, 407251000, 407281008, … |
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | LPSN | 776052 | 515602 | 777447 | | | | | | | 1 | | | B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | LPSN | 776052 | 515602 | 777447 | | | | | | | 1 | |
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | LPSN | 776053 | 515602 | | | | | 5427575 | | | 1 | 419388003 | | B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | LPSN | 776053 | 515602 | | | | | 5427575 | | | 1 | 419388003 |
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | LPSN | 776056 | 515602 | 788468 | | | | | | | 1 | | | B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | LPSN | 776056 | 515602 | 788468 | | | | | | | 1 | |
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | LPSN | 776057 | 515602 | | | | | 11286021 | | | 1 | 1095001000112106, 715307006, 737528008, … | | B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | LPSN | 776057 | 515602 | | | | | 11286021 | | | 1 | 1095001000112106, 715307006, 737528008, … |
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | GBIF | | 776057 | | | | | 12233256 | 11286021 | | 1 | | | B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | GBIF | | 776057 | | | | | 12233256 | 11286021 | | 1 | |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -134,14 +134,14 @@ as comma separated values.
**Example content** **Example content**
| ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc | | ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|:---:|:--------:|:---------------------------:|:----------------------------------------------:|:------------------------------:|:-------------------------------------------:|:------------------------------------------------------------:|:-------------------:|:-------------------------------------------------------:|:--------:|:----------:|:------:|:--------:|:------------------------------:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … | | | 1.0 | g | 101493-5, 11-7, 12-5, … | | AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … | | | 1.0 | g | 101493-5, 11-7, 12-5, … |
| AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … | | AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
| AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g | | | AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g | |
| AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … | | AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
| AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … | | AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
| PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … | | | 3.6 | g | | | PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … | | | 3.6 | g | |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -186,14 +186,14 @@ here](https://amr-for-r.org/reference/clinical_breakpoints.html).
**Example content** **Example content**
| guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD | | guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
|:-----------:|:-----:|:-----:|:------:|:----:|:-------------:|:--------------------------:|:----------:|:---:|:-----------------------------:|:---------------:|:--------------:|:------------:|:------------:|:-----:|:------:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE | | EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | | 1.000 | 4.000 | FALSE | FALSE | | EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | | 1.000 | 4.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE | | EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | | 0.125 | 0.125 | FALSE | FALSE | | EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | | 0.125 | 0.125 | FALSE | FALSE |
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE | | EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | | 4.000 | 4.000 | FALSE | FALSE | | EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | | 4.000 | 4.000 | FALSE | FALSE |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -236,14 +236,14 @@ here](https://amr-for-r.org/reference/microorganisms.groups.html).
**Example content** **Example content**
| mo_group | mo | mo_group_name | mo_name | | mo_group | mo | mo_group_name | mo_name |
|:--------------:|:------------:|:-------------------------------:|:---------------------------:| |:--:|:--:|:--:|:--:|
| B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii | | B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
| B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus | | B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
| B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae | | B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
| B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis | | B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
| B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii | | B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
| B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii | | B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -394,14 +394,14 @@ here](https://amr-for-r.org/reference/dosage.html).
**Example content** **Example content**
| ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version | | ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
|:---:|:-----------:|:-----------------:|:-----------:|:----------:|:--------------:|:-----:|:------------------:|:--------------:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv | | 25-30 mg/kg x 1 iv | 15 | | AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv | | 25-30 mg/kg x 1 iv | 15 |
| AMX | Amoxicillin | high_dosage | 2 g | 6 | iv | | 2 g x 6 iv | 15 | | AMX | Amoxicillin | high_dosage | 2 g | 6 | iv | | 2 g x 6 iv | 15 |
| AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv | | 1 g x 3-4 iv | 15 | | AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv | | 1 g x 3-4 iv | 15 |
| AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral | | 0.75-1 g x 3 oral | 15 | | AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral | | 0.75-1 g x 3 oral | 15 |
| AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 | | AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
| AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 | | AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -424,14 +424,14 @@ here](https://amr-for-r.org/reference/example_isolates.html).
**Example content** **Example content**
| date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF | | date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
|:----------:|:-------:|:---:|:------:|:--------:|:------------:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R | | 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
| 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R | | 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | | | 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | | | 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | | R | | | | | R | | | | 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | | R | | | | | R | | |
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | R | R | | | | | R | | | | 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | R | R | | | | | R | | |
------------------------------------------------------------------------ ------------------------------------------------------------------------
@@ -555,11 +555,11 @@ contain the trade names and LOINC codes as comma separated values.
**Example content** **Example content**
| av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc | | av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|:---:|:------------------:|:-------:|:---------:|:------------------------------------------------------------------:|:-----------------------------------------------------:|:--------:|:----------:|:------:|:--------:|:----------------------------:| |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g | | | 29113-8, 30273-7, 30287-7, … | | ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g | | | 29113-8, 30273-7, 30287-7, … |
| ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g | | | ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g | |
| ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg | | | | | ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg | | | |
| AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g | | | | | AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g | | | |
| AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g | | | 29114-6, 30296-8, 30297-6, … | | AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g | | | 29114-6, 30296-8, 30297-6, … |
| ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g | | | | | ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g | | | |

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articles/index.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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authors.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

139
index.html
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@@ -33,7 +33,7 @@
<a class="navbar-brand me-2" href="index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -145,24 +145,26 @@
<span><span class="co">#&gt; ℹ Using column mo as input for `mo_fullname()`</span></span> <span><span class="co">#&gt; ℹ Using column mo as input for `mo_fullname()`</span></span>
<span><span class="co">#&gt; ℹ Using column mo as input for `mo_is_gram_negative()`</span></span> <span><span class="co">#&gt; ℹ Using column mo as input for `mo_is_gram_negative()`</span></span>
<span><span class="co">#&gt; ℹ Using column mo as input for `mo_is_intrinsic_resistant()`</span></span> <span><span class="co">#&gt; ℹ Using column mo as input for `mo_is_intrinsic_resistant()`</span></span>
<span><span class="co">#&gt; ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant</span></span> <span><span class="co">#&gt; ℹ Determining intrinsic resistance based on 'EUCAST Expected</span></span>
<span><span class="co">#&gt; Phenotypes' v1.2 (2023). This note will be shown once per session.</span></span> <span><span class="co">#&gt; Resistant Phenotypes' v1.2 (2023). This note will be shown</span></span>
<span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK</span></span> <span><span class="co">#&gt; once per session.</span></span>
<span><span class="co">#&gt; (amikacin), and KAN (kanamycin)</span></span> <span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB</span></span>
<span><span class="co">#&gt; ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)</span></span> <span><span class="co">#&gt; (tobramycin), AMK (amikacin), and KAN (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For `carbapenems()` using columns IPM (imipenem) and MEM</span></span>
<span><span class="co">#&gt; (meropenem)</span></span>
<span><span class="co">#&gt; # A tibble: 35 × 7</span></span> <span><span class="co">#&gt; # A tibble: 35 × 7</span></span>
<span><span class="co">#&gt; bacteria GEN TOB AMK KAN IPM MEM </span></span> <span><span class="co">#&gt; bacteria GEN TOB AMK KAN IPM MEM </span></span>
<span><span class="co">#&gt; &lt;chr&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt;</span></span> <span><span class="co">#&gt; &lt;chr&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt; &lt;sir&gt;</span></span>
<span><span class="co">#&gt; 1 Pseudomonas aeruginosa I S NA R S NA </span></span> <span><span class="co">#&gt; 1 Pseudomonas aer… I S NA R S NA </span></span>
<span><span class="co">#&gt; 2 Pseudomonas aeruginosa I S NA R S NA </span></span> <span><span class="co">#&gt; 2 Pseudomonas aer… I S NA R S NA </span></span>
<span><span class="co">#&gt; 3 Pseudomonas aeruginosa I S NA R S NA </span></span> <span><span class="co">#&gt; 3 Pseudomonas aer… I S NA R S NA </span></span>
<span><span class="co">#&gt; 4 Pseudomonas aeruginosa S S S R NA S </span></span> <span><span class="co">#&gt; 4 Pseudomonas aer… S S S R NA S </span></span>
<span><span class="co">#&gt; 5 Pseudomonas aeruginosa S S S R S S </span></span> <span><span class="co">#&gt; 5 Pseudomonas aer… S S S R S S </span></span>
<span><span class="co">#&gt; 6 Pseudomonas aeruginosa S S S R S S </span></span> <span><span class="co">#&gt; 6 Pseudomonas aer… S S S R S S </span></span>
<span><span class="co">#&gt; 7 Stenotrophomonas maltophilia R R R R R R </span></span> <span><span class="co">#&gt; 7 Stenotrophomona… R R R R R R </span></span>
<span><span class="co">#&gt; 8 Pseudomonas aeruginosa S S S R NA S </span></span> <span><span class="co">#&gt; 8 Pseudomonas aer… S S S R NA S </span></span>
<span><span class="co">#&gt; 9 Pseudomonas aeruginosa S S S R NA S </span></span> <span><span class="co">#&gt; 9 Pseudomonas aer… S S S R NA S </span></span>
<span><span class="co">#&gt; 10 Pseudomonas aeruginosa S S S R S S </span></span> <span><span class="co">#&gt; 10 Pseudomonas aer… S S S R S S </span></span>
<span><span class="co">#&gt; # ℹ 25 more rows</span></span></code></pre></div> <span><span class="co">#&gt; # ℹ 25 more rows</span></span></code></pre></div>
<p>With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (<code><a href="reference/mo_property.html">mo_is_gram_negative()</a></code> and <code><a href="reference/mo_property.html">mo_is_intrinsic_resistant()</a></code>) and a column selection on two antibiotic groups (<code><a href="reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="reference/antimicrobial_selectors.html">carbapenems()</a></code>), the reference data about <a href="./reference/microorganisms.html">all microorganisms</a> and <a href="./reference/antimicrobials.html">all antimicrobials</a> in the <code>AMR</code> package make sure you get what you meant.</p> <p>With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (<code><a href="reference/mo_property.html">mo_is_gram_negative()</a></code> and <code><a href="reference/mo_property.html">mo_is_intrinsic_resistant()</a></code>) and a column selection on two antibiotic groups (<code><a href="reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="reference/antimicrobial_selectors.html">carbapenems()</a></code>), the reference data about <a href="./reference/microorganisms.html">all microorganisms</a> and <a href="./reference/antimicrobials.html">all antimicrobials</a> in the <code>AMR</code> package make sure you get what you meant.</p>
</div> </div>
@@ -174,20 +176,21 @@
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span> <code class="sourceCode R"><span><span class="fu"><a href="reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> <span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK</span></span> <span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB</span></span>
<span><span class="co">#&gt; (amikacin), and KAN (kanamycin)</span></span> <span><span class="co">#&gt; (tobramycin), AMK (amikacin), and KAN (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)</span></span></code></pre></div> <span><span class="co">#&gt; ℹ For `carbapenems()` using columns IPM (imipenem) and MEM</span></span>
<table style="width:100%;" class="table"> <span><span class="co">#&gt; (meropenem)</span></span></code></pre></div>
<table class="table">
<colgroup> <colgroup>
<col width="12%">
<col width="15%">
<col width="14%"> <col width="14%">
<col width="14%"> <col width="15%">
<col width="14%"> <col width="11%">
<col width="14%"> <col width="15%">
<col width="14%">
<col width="14%">
<col width="14%"> <col width="14%">
</colgroup> </colgroup>
<thead><tr class="header"> <thead><tr>
<th align="left">Pathogen</th> <th align="left">Pathogen</th>
<th align="left">Amikacin</th> <th align="left">Amikacin</th>
<th align="left">Gentamicin</th> <th align="left">Gentamicin</th>
@@ -197,7 +200,7 @@
<th align="left">Tobramycin</th> <th align="left">Tobramycin</th>
</tr></thead> </tr></thead>
<tbody> <tbody>
<tr class="odd"> <tr>
<td align="left">CoNS</td> <td align="left">CoNS</td>
<td align="left">0% (0-8%,N=43)</td> <td align="left">0% (0-8%,N=43)</td>
<td align="left">86% (82-90%,N=309)</td> <td align="left">86% (82-90%,N=309)</td>
@@ -206,7 +209,7 @@
<td align="left">52% (37-67%,N=48)</td> <td align="left">52% (37-67%,N=48)</td>
<td align="left">22% (12-35%,N=55)</td> <td align="left">22% (12-35%,N=55)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left"><em>E. coli</em></td> <td align="left"><em>E. coli</em></td>
<td align="left">100% (98-100%,N=171)</td> <td align="left">100% (98-100%,N=171)</td>
<td align="left">98% (96-99%,N=460)</td> <td align="left">98% (96-99%,N=460)</td>
@@ -215,7 +218,7 @@
<td align="left">100% (99-100%,N=418)</td> <td align="left">100% (99-100%,N=418)</td>
<td align="left">97% (96-99%,N=462)</td> <td align="left">97% (96-99%,N=462)</td>
</tr> </tr>
<tr class="odd"> <tr>
<td align="left"><em>E. faecalis</em></td> <td align="left"><em>E. faecalis</em></td>
<td align="left">0% (0-9%,N=39)</td> <td align="left">0% (0-9%,N=39)</td>
<td align="left">0% (0-9%,N=39)</td> <td align="left">0% (0-9%,N=39)</td>
@@ -224,7 +227,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">0% (0-9%,N=39)</td> <td align="left">0% (0-9%,N=39)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left"><em>K. pneumoniae</em></td> <td align="left"><em>K. pneumoniae</em></td>
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">90% (79-96%,N=58)</td> <td align="left">90% (79-96%,N=58)</td>
@@ -233,7 +236,7 @@
<td align="left">100% (93-100%,N=53)</td> <td align="left">100% (93-100%,N=53)</td>
<td align="left">90% (79-96%,N=58)</td> <td align="left">90% (79-96%,N=58)</td>
</tr> </tr>
<tr class="odd"> <tr>
<td align="left"><em>P. aeruginosa</em></td> <td align="left"><em>P. aeruginosa</em></td>
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td> <td align="left">100% (88-100%,N=30)</td>
@@ -242,7 +245,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">100% (88-100%,N=30)</td> <td align="left">100% (88-100%,N=30)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left"><em>P. mirabilis</em></td> <td align="left"><em>P. mirabilis</em></td>
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">94% (80-99%,N=34)</td> <td align="left">94% (80-99%,N=34)</td>
@@ -251,7 +254,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">94% (80-99%,N=34)</td> <td align="left">94% (80-99%,N=34)</td>
</tr> </tr>
<tr class="odd"> <tr>
<td align="left"><em>S. aureus</em></td> <td align="left"><em>S. aureus</em></td>
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">99% (97-100%,N=233)</td> <td align="left">99% (97-100%,N=233)</td>
@@ -260,7 +263,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">98% (92-100%,N=86)</td> <td align="left">98% (92-100%,N=86)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left"><em>S. epidermidis</em></td> <td align="left"><em>S. epidermidis</em></td>
<td align="left">0% (0-8%,N=44)</td> <td align="left">0% (0-8%,N=44)</td>
<td align="left">79% (71-85%,N=163)</td> <td align="left">79% (71-85%,N=163)</td>
@@ -269,7 +272,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">51% (40-61%,N=89)</td> <td align="left">51% (40-61%,N=89)</td>
</tr> </tr>
<tr class="odd"> <tr>
<td align="left"><em>S. hominis</em></td> <td align="left"><em>S. hominis</em></td>
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">92% (84-97%,N=80)</td> <td align="left">92% (84-97%,N=80)</td>
@@ -278,7 +281,7 @@
<td align="left">NA</td> <td align="left">NA</td>
<td align="left">85% (74-93%,N=62)</td> <td align="left">85% (74-93%,N=62)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left"><em>S. pneumoniae</em></td> <td align="left"><em>S. pneumoniae</em></td>
<td align="left">0% (0-3%,N=117)</td> <td align="left">0% (0-3%,N=117)</td>
<td align="left">0% (0-3%,N=117)</td> <td align="left">0% (0-3%,N=117)</td>
@@ -296,25 +299,25 @@
<span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span></code></pre></div> <span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span><span class="op">)</span></span></code></pre></div>
<table class="table"> <table class="table">
<colgroup> <colgroup>
<col width="25%"> <col width="12%">
<col width="25%"> <col width="21%">
<col width="25%"> <col width="32%">
<col width="25%"> <col width="32%">
</colgroup> </colgroup>
<thead><tr class="header"> <thead><tr>
<th align="left">Pathogen</th> <th align="left">Pathogen</th>
<th align="left">Piperacillin/tazobactam</th> <th align="left">Piperacillin/tazobactam</th>
<th align="left">Piperacillin/tazobactam + Gentamicin</th> <th align="left">Piperacillin/tazobactam + Gentamicin</th>
<th align="left">Piperacillin/tazobactam + Tobramycin</th> <th align="left">Piperacillin/tazobactam + Tobramycin</th>
</tr></thead> </tr></thead>
<tbody> <tbody>
<tr class="odd"> <tr>
<td align="left">Gram-negative</td> <td align="left">Gram-negative</td>
<td align="left">88% (85-91%,N=641)</td> <td align="left">88% (85-91%,N=641)</td>
<td align="left">99% (97-99%,N=691)</td> <td align="left">99% (97-99%,N=691)</td>
<td align="left">98% (97-99%,N=693)</td> <td align="left">98% (97-99%,N=693)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left">Gram-positive</td> <td align="left">Gram-positive</td>
<td align="left">86% (82-89%,N=345)</td> <td align="left">86% (82-89%,N=345)</td>
<td align="left">98% (96-98%,N=1044)</td> <td align="left">98% (96-98%,N=1044)</td>
@@ -336,20 +339,20 @@
<col width="26%"> <col width="26%">
<col width="26%"> <col width="26%">
</colgroup> </colgroup>
<thead><tr class="header"> <thead><tr>
<th align="left">Збудник</th> <th align="left">Збудник</th>
<th align="left">Гентаміцин</th> <th align="left">Гентаміцин</th>
<th align="left">Тобраміцин</th> <th align="left">Тобраміцин</th>
<th align="left">Ципрофлоксацин</th> <th align="left">Ципрофлоксацин</th>
</tr></thead> </tr></thead>
<tbody> <tbody>
<tr class="odd"> <tr>
<td align="left">Грамнегативні</td> <td align="left">Грамнегативні</td>
<td align="left">96% (95-98%,N=684)</td> <td align="left">96% (95-98%,N=684)</td>
<td align="left">96% (94-97%,N=686)</td> <td align="left">96% (94-97%,N=686)</td>
<td align="left">91% (88-93%,N=684)</td> <td align="left">91% (88-93%,N=684)</td>
</tr> </tr>
<tr class="even"> <tr>
<td align="left">Грампозитивні</td> <td align="left">Грампозитивні</td>
<td align="left">63% (60-66%,N=1170)</td> <td align="left">63% (60-66%,N=1170)</td>
<td align="left">34% (31-38%,N=665)</td> <td align="left">34% (31-38%,N=665)</td>
@@ -404,16 +407,18 @@
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">GEN</span>, <span class="va">TOB</span><span class="op">)</span>,</span> <span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">GEN</span>, <span class="va">TOB</span><span class="op">)</span>,</span>
<span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>total_R <span class="op">=</span> <span class="va">resistance</span>,</span> <span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>total_R <span class="op">=</span> <span class="va">resistance</span>,</span>
<span> conf_int <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="reference/proportion.html">sir_confidence_interval</a></span><span class="op">(</span><span class="va">x</span>, collapse <span class="op">=</span> <span class="st">"-"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> <span> conf_int <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="reference/proportion.html">sir_confidence_interval</a></span><span class="op">(</span><span class="va">x</span>, collapse <span class="op">=</span> <span class="st">"-"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span> <span><span class="co">#&gt; ℹ `resistance()` assumes the EUCAST guideline and thus</span></span>
<span><span class="co">#&gt; category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span> <span><span class="co">#&gt; considers the 'I' category susceptible. Set the `guideline`</span></span>
<span><span class="co">#&gt; option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span> <span><span class="co">#&gt; argument or the `AMR_guideline` option to either "CLSI" or</span></span>
<span><span class="co">#&gt; "EUCAST", see `?AMR-options`.</span></span>
<span><span class="co">#&gt; ℹ This message will be shown once per session.</span></span> <span><span class="co">#&gt; ℹ This message will be shown once per session.</span></span>
<span><span class="co">#&gt; # A tibble: 3 × 5</span></span> <span><span class="co">#&gt; # A tibble: 3 × 5</span></span>
<span><span class="co">#&gt; ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int</span></span> <span><span class="co">#&gt; ward GEN_total_R GEN_conf_int TOB_total_R</span></span>
<span><span class="co">#&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; </span></span> <span><span class="co">#&gt; &lt;chr&gt; &lt;dbl&gt; &lt;chr&gt; &lt;dbl&gt;</span></span>
<span><span class="co">#&gt; 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347 </span></span> <span><span class="co">#&gt; 1 Clinical 0.229 0.205-0.254 0.315</span></span>
<span><span class="co">#&gt; 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449 </span></span> <span><span class="co">#&gt; 2 ICU 0.290 0.253-0.33 0.400</span></span>
<span><span class="co">#&gt; 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493</span></span></code></pre></div> <span><span class="co">#&gt; 3 Outpatient 0.2 0.131-0.285 0.368</span></span>
<span><span class="co">#&gt; # ℹ 1 more variable: TOB_conf_int &lt;chr&gt;</span></span></code></pre></div>
<p>Or use <a href="https://amr-for-r.org/reference/antimicrobial_selectors.html">antimicrobial selectors</a> to select a series of antibiotic columns:</p> <p>Or use <a href="https://amr-for-r.org/reference/antimicrobial_selectors.html">antimicrobial selectors</a> to select a series of antibiotic columns:</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><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://amr-for-r.org">AMR</a></span><span class="op">)</span></span> <code class="sourceCode R"><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://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
@@ -425,15 +430,16 @@
<span> <span class="co"># calculate AMR using resistance(), over all aminoglycosides and polymyxins:</span></span> <span> <span class="co"># calculate AMR using resistance(), over all aminoglycosides and polymyxins:</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antimicrobial_selectors.html">polymyxins</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span> <span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="reference/antimicrobial_selectors.html">polymyxins</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
<span> <span class="va">resistance</span><span class="op">)</span><span class="op">)</span></span> <span> <span class="va">resistance</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK</span></span> <span><span class="co">#&gt; ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB</span></span>
<span><span class="co">#&gt; (amikacin), and KAN (kanamycin)</span></span> <span><span class="co">#&gt; (tobramycin), AMK (amikacin), and KAN (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For `polymyxins()` using column COL (colistin)</span></span> <span><span class="co">#&gt; ℹ For `polymyxins()` using column COL (colistin)</span></span>
<span><span class="co">#&gt; Warning: There was 1 warning in `summarise()`.</span></span> <span><span class="co">#&gt; Warning: There was 1 warning in `summarise()`.</span></span>
<span><span class="co">#&gt; ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.</span></span> <span><span class="co">#&gt; ℹ In argument: `across(c(aminoglycosides(), polymyxins()),</span></span>
<span><span class="co">#&gt; resistance)`.</span></span>
<span><span class="co">#&gt; ℹ In group 3: `ward = "Outpatient"`.</span></span> <span><span class="co">#&gt; ℹ In group 3: `ward = "Outpatient"`.</span></span>
<span><span class="co">#&gt; Caused by warning:</span></span> <span><span class="co">#&gt; Caused by warning:</span></span>
<span><span class="co">#&gt; ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient"</span></span> <span><span class="co">#&gt; ! Introducing NA: only 23 results available for KAN in group:</span></span>
<span><span class="co">#&gt; (whilst `minimum = 30`).</span></span> <span><span class="co">#&gt; ward = "Outpatient" (whilst `minimum = 30`).</span></span>
<span><span class="va">out</span></span> <span><span class="va">out</span></span>
<span><span class="co">#&gt; # A tibble: 3 × 6</span></span> <span><span class="co">#&gt; # A tibble: 3 × 6</span></span>
<span><span class="co">#&gt; ward GEN TOB AMK KAN COL</span></span> <span><span class="co">#&gt; ward GEN TOB AMK KAN COL</span></span>
@@ -445,11 +451,12 @@
<code class="sourceCode R"><span><span class="co"># transform the antibiotic columns to names:</span></span> <code class="sourceCode R"><span><span class="co"># transform the antibiotic columns to names:</span></span>
<span><span class="va">out</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="reference/ab_property.html">set_ab_names</a></span><span class="op">(</span><span class="op">)</span></span> <span><span class="va">out</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="reference/ab_property.html">set_ab_names</a></span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co">#&gt; # A tibble: 3 × 6</span></span> <span><span class="co">#&gt; # A tibble: 3 × 6</span></span>
<span><span class="co">#&gt; ward gentamicin tobramycin amikacin kanamycin colistin</span></span> <span><span class="co">#&gt; ward gentamicin tobramycin amikacin kanamycin</span></span>
<span><span class="co">#&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span></span> <span><span class="co">#&gt; &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;</span></span>
<span><span class="co">#&gt; 1 Clinical 0.229 0.315 0.626 1 0.780</span></span> <span><span class="co">#&gt; 1 Clinical 0.229 0.315 0.626 1</span></span>
<span><span class="co">#&gt; 2 ICU 0.290 0.400 0.662 1 0.857</span></span> <span><span class="co">#&gt; 2 ICU 0.290 0.400 0.662 1</span></span>
<span><span class="co">#&gt; 3 Outpatient 0.2 0.368 0.605 NA 0.889</span></span></code></pre></div> <span><span class="co">#&gt; 3 Outpatient 0.2 0.368 0.605 NA</span></span>
<span><span class="co">#&gt; # ℹ 1 more variable: colistin &lt;dbl&gt;</span></span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># transform the antibiotic column to ATC codes:</span></span> <code class="sourceCode R"><span><span class="co"># transform the antibiotic column to ATC codes:</span></span>
<span><span class="va">out</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="reference/ab_property.html">set_ab_names</a></span><span class="op">(</span>property <span class="op">=</span> <span class="st">"atc"</span><span class="op">)</span></span> <span><span class="va">out</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="reference/ab_property.html">set_ab_names</a></span><span class="op">(</span>property <span class="op">=</span> <span class="st">"atc"</span><span class="op">)</span></span>

126
index.md
View File

@@ -100,6 +100,7 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
which work in base R, `dplyr` and `data.table`. which work in base R, `dplyr` and `data.table`.
``` r ``` r
# AMR works great with dplyr, but it's not required or neccesary # AMR works great with dplyr, but it's not required or neccesary
library(AMR) library(AMR)
library(dplyr, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE)
@@ -116,24 +117,26 @@ example_isolates %>%
#> ℹ Using column mo as input for `mo_fullname()` #> ℹ Using column mo as input for `mo_fullname()`
#> ℹ Using column mo as input for `mo_is_gram_negative()` #> ℹ Using column mo as input for `mo_is_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant #> ℹ Determining intrinsic resistance based on 'EUCAST Expected
#> Phenotypes' v1.2 (2023). This note will be shown once per session. #> Resistant Phenotypes' v1.2 (2023). This note will be shown
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> once per session.
#> (amikacin), and KAN (kanamycin) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> # A tibble: 35 × 7 #> # A tibble: 35 × 7
#> bacteria GEN TOB AMK KAN IPM MEM #> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir> #> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 Pseudomonas aeruginosa I S NA R S NA #> 1 Pseudomonas aer… I S NA R S NA
#> 2 Pseudomonas aeruginosa I S NA R S NA #> 2 Pseudomonas aer… I S NA R S NA
#> 3 Pseudomonas aeruginosa I S NA R S NA #> 3 Pseudomonas aer… I S NA R S NA
#> 4 Pseudomonas aeruginosa S S S R NA S #> 4 Pseudomonas aer… S S S R NA S
#> 5 Pseudomonas aeruginosa S S S R S S #> 5 Pseudomonas aer… S S S R S S
#> 6 Pseudomonas aeruginosa S S S R S S #> 6 Pseudomonas aer… S S S R S S
#> 7 Stenotrophomonas maltophilia R R R R R R #> 7 Stenotrophomona… R R R R R R
#> 8 Pseudomonas aeruginosa S S S R NA S #> 8 Pseudomonas aer… S S S R NA S
#> 9 Pseudomonas aeruginosa S S S R NA S #> 9 Pseudomonas aer… S S S R NA S
#> 10 Pseudomonas aeruginosa S S S R S S #> 10 Pseudomonas aer… S S S R S S
#> # ℹ 25 more rows #> # ℹ 25 more rows
``` ```
@@ -161,39 +164,42 @@ If used inside [R Markdown](https://rmarkdown.rstudio.com) or
output format automatically (such as markdown, LaTeX, HTML, etc.). output format automatically (such as markdown, LaTeX, HTML, etc.).
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems())) antimicrobials = c(aminoglycosides(), carbapenems()))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (amikacin), and KAN (kanamycin) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
``` ```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------| |:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | | CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | | *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) | | *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) | | *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) | | *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) | | *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) | | *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) | | *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) | | *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | | *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
In combination antibiograms, it is clear that combined antimicrobials In combination antibiograms, it is clear that combined antimicrobials
yield higher empiric coverage: yield higher empiric coverage:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain") mo_transform = "gramstain")
``` ```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:--------------|:------------------------|:-------------------------------------|:-------------------------------------| |:---|:---|:---|:---|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) | | Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) | | Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
Like many other functions in this package, Like many other functions in this package,
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md) comes [`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md) comes
@@ -201,6 +207,7 @@ with support for 28 languages that are often detected automatically
based on system language: based on system language:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
mo_transform = "gramstain", mo_transform = "gramstain",
@@ -221,6 +228,7 @@ with new scale functions, to allow plotting of log2-distributed MIC
values and SIR values. values and SIR values.
``` r ``` r
library(ggplot2) library(ggplot2)
library(AMR) library(AMR)
@@ -258,6 +266,7 @@ For a manual approach, you can use the `resistance` or
function: function:
``` r ``` r
example_isolates %>% example_isolates %>%
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) %>%
@@ -266,16 +275,18 @@ example_isolates %>%
summarise(across(c(GEN, TOB), summarise(across(c(GEN, TOB),
list(total_R = resistance, list(total_R = resistance,
conf_int = function(x) sir_confidence_interval(x, collapse = "-")))) conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> ℹ `resistance()` assumes the EUCAST guideline and thus
#> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> considers the 'I' category susceptible. Set the `guideline`
#> option to either "CLSI" or "EUCAST", see `?AMR-options`. #> argument or the `AMR_guideline` option to either "CLSI" or
#> "EUCAST", see `?AMR-options`.
#> ℹ This message will be shown once per session. #> ℹ This message will be shown once per session.
#> # A tibble: 3 × 5 #> # A tibble: 3 × 5
#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int #> ward GEN_total_R GEN_conf_int TOB_total_R
#> <chr> <dbl> <chr> <dbl> <chr> #> <chr> <dbl> <chr> <dbl>
#> 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347 #> 1 Clinical 0.229 0.205-0.254 0.315
#> 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449 #> 2 ICU 0.290 0.253-0.33 0.400
#> 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493 #> 3 Outpatient 0.2 0.131-0.285 0.368
#> # ℹ 1 more variable: TOB_conf_int <chr>
``` ```
Or use [antimicrobial Or use [antimicrobial
@@ -283,6 +294,7 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html)
to select a series of antibiotic columns: to select a series of antibiotic columns:
``` r ``` r
library(AMR) library(AMR)
library(dplyr) library(dplyr)
@@ -292,15 +304,16 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins: # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()), summarise(across(c(aminoglycosides(), polymyxins()),
resistance)) resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (amikacin), and KAN (kanamycin) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin) #> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`. #> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`. #> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
#> resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`. #> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning: #> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" #> ! Introducing NA: only 23 results available for KAN in group:
#> (whilst `minimum = 30`). #> ward = "Outpatient" (whilst `minimum = 30`).
out out
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL #> ward GEN TOB AMK KAN COL
@@ -311,17 +324,20 @@ out
``` ```
``` r ``` r
# transform the antibiotic columns to names: # transform the antibiotic columns to names:
out %>% set_ab_names() out %>% set_ab_names()
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward gentamicin tobramycin amikacin kanamycin colistin #> ward gentamicin tobramycin amikacin kanamycin
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1 0.780 #> 1 Clinical 0.229 0.315 0.626 1
#> 2 ICU 0.290 0.400 0.662 1 0.857 #> 2 ICU 0.290 0.400 0.662 1
#> 3 Outpatient 0.2 0.368 0.605 NA 0.889 #> 3 Outpatient 0.2 0.368 0.605 NA
#> # ℹ 1 more variable: colistin <dbl>
``` ```
``` r ``` r
# transform the antibiotic column to ATC codes: # transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc") out %>% set_ab_names(property = "atc")
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
@@ -393,6 +409,7 @@ This package is available [here on the official R network
R from CRAN by using the command: R from CRAN by using the command:
``` r ``` r
install.packages("AMR") install.packages("AMR")
``` ```
@@ -417,6 +434,7 @@ here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
To install the latest and unpublished beta version: To install the latest and unpublished beta version:
``` r ``` r
install.packages("AMR", repos = "beta.amr-for-r.org") install.packages("AMR", repos = "beta.amr-for-r.org")
# if this does not work, try to install directly from GitHub using the 'remotes' package: # if this does not work, try to install directly from GitHub using the 'remotes' package:

126
llms.txt
View File

@@ -100,6 +100,7 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html),
which work in base R, `dplyr` and `data.table`. which work in base R, `dplyr` and `data.table`.
``` r ``` r
# AMR works great with dplyr, but it's not required or neccesary # AMR works great with dplyr, but it's not required or neccesary
library(AMR) library(AMR)
library(dplyr, warn.conflicts = FALSE) library(dplyr, warn.conflicts = FALSE)
@@ -116,24 +117,26 @@ example_isolates %>%
#> ℹ Using column mo as input for `mo_fullname()` #> ℹ Using column mo as input for `mo_fullname()`
#> ℹ Using column mo as input for `mo_is_gram_negative()` #> ℹ Using column mo as input for `mo_is_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant #> ℹ Determining intrinsic resistance based on 'EUCAST Expected
#> Phenotypes' v1.2 (2023). This note will be shown once per session. #> Resistant Phenotypes' v1.2 (2023). This note will be shown
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> once per session.
#> (amikacin), and KAN (kanamycin) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> # A tibble: 35 × 7 #> # A tibble: 35 × 7
#> bacteria GEN TOB AMK KAN IPM MEM #> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir> #> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
#> 1 Pseudomonas aeruginosa I S NA R S NA #> 1 Pseudomonas aer… I S NA R S NA
#> 2 Pseudomonas aeruginosa I S NA R S NA #> 2 Pseudomonas aer… I S NA R S NA
#> 3 Pseudomonas aeruginosa I S NA R S NA #> 3 Pseudomonas aer… I S NA R S NA
#> 4 Pseudomonas aeruginosa S S S R NA S #> 4 Pseudomonas aer… S S S R NA S
#> 5 Pseudomonas aeruginosa S S S R S S #> 5 Pseudomonas aer… S S S R S S
#> 6 Pseudomonas aeruginosa S S S R S S #> 6 Pseudomonas aer… S S S R S S
#> 7 Stenotrophomonas maltophilia R R R R R R #> 7 Stenotrophomona… R R R R R R
#> 8 Pseudomonas aeruginosa S S S R NA S #> 8 Pseudomonas aer… S S S R NA S
#> 9 Pseudomonas aeruginosa S S S R NA S #> 9 Pseudomonas aer… S S S R NA S
#> 10 Pseudomonas aeruginosa S S S R S S #> 10 Pseudomonas aer… S S S R S S
#> # ℹ 25 more rows #> # ℹ 25 more rows
``` ```
@@ -161,39 +164,42 @@ If used inside [R Markdown](https://rmarkdown.rstudio.com) or
output format automatically (such as markdown, LaTeX, HTML, etc.). output format automatically (such as markdown, LaTeX, HTML, etc.).
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems())) antimicrobials = c(aminoglycosides(), carbapenems()))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (amikacin), and KAN (kanamycin) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
``` ```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------| |:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | | CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | | *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) | | *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) | | *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) | | *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) | | *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) | | *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) | | *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) | | *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | | *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
In combination antibiograms, it is clear that combined antimicrobials In combination antibiograms, it is clear that combined antimicrobials
yield higher empiric coverage: yield higher empiric coverage:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain") mo_transform = "gramstain")
``` ```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:--------------|:------------------------|:-------------------------------------|:-------------------------------------| |:---|:---|:---|:---|
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) | | Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) | | Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
Like many other functions in this package, Like many other functions in this package,
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md) comes [`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md) comes
@@ -201,6 +207,7 @@ with support for 28 languages that are often detected automatically
based on system language: based on system language:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
mo_transform = "gramstain", mo_transform = "gramstain",
@@ -221,6 +228,7 @@ with new scale functions, to allow plotting of log2-distributed MIC
values and SIR values. values and SIR values.
``` r ``` r
library(ggplot2) library(ggplot2)
library(AMR) library(AMR)
@@ -258,6 +266,7 @@ For a manual approach, you can use the `resistance` or
function: function:
``` r ``` r
example_isolates %>% example_isolates %>%
# group by ward: # group by ward:
group_by(ward) %>% group_by(ward) %>%
@@ -266,16 +275,18 @@ example_isolates %>%
summarise(across(c(GEN, TOB), summarise(across(c(GEN, TOB),
list(total_R = resistance, list(total_R = resistance,
conf_int = function(x) sir_confidence_interval(x, collapse = "-")))) conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> ℹ `resistance()` assumes the EUCAST guideline and thus
#> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> considers the 'I' category susceptible. Set the `guideline`
#> option to either "CLSI" or "EUCAST", see `?AMR-options`. #> argument or the `AMR_guideline` option to either "CLSI" or
#> "EUCAST", see `?AMR-options`.
#> ℹ This message will be shown once per session. #> ℹ This message will be shown once per session.
#> # A tibble: 3 × 5 #> # A tibble: 3 × 5
#> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int #> ward GEN_total_R GEN_conf_int TOB_total_R
#> <chr> <dbl> <chr> <dbl> <chr> #> <chr> <dbl> <chr> <dbl>
#> 1 Clinical 0.229 0.205-0.254 0.315 0.284-0.347 #> 1 Clinical 0.229 0.205-0.254 0.315
#> 2 ICU 0.290 0.253-0.33 0.400 0.353-0.449 #> 2 ICU 0.290 0.253-0.33 0.400
#> 3 Outpatient 0.2 0.131-0.285 0.368 0.254-0.493 #> 3 Outpatient 0.2 0.131-0.285 0.368
#> # ℹ 1 more variable: TOB_conf_int <chr>
``` ```
Or use [antimicrobial Or use [antimicrobial
@@ -283,6 +294,7 @@ selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html)
to select a series of antibiotic columns: to select a series of antibiotic columns:
``` r ``` r
library(AMR) library(AMR)
library(dplyr) library(dplyr)
@@ -292,15 +304,16 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins: # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()), summarise(across(c(aminoglycosides(), polymyxins()),
resistance)) resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (amikacin), and KAN (kanamycin) #> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin) #> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`. #> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`. #> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
#> resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`. #> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning: #> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" #> ! Introducing NA: only 23 results available for KAN in group:
#> (whilst `minimum = 30`). #> ward = "Outpatient" (whilst `minimum = 30`).
out out
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL #> ward GEN TOB AMK KAN COL
@@ -311,17 +324,20 @@ out
``` ```
``` r ``` r
# transform the antibiotic columns to names: # transform the antibiotic columns to names:
out %>% set_ab_names() out %>% set_ab_names()
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
#> ward gentamicin tobramycin amikacin kanamycin colistin #> ward gentamicin tobramycin amikacin kanamycin
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Clinical 0.229 0.315 0.626 1 0.780 #> 1 Clinical 0.229 0.315 0.626 1
#> 2 ICU 0.290 0.400 0.662 1 0.857 #> 2 ICU 0.290 0.400 0.662 1
#> 3 Outpatient 0.2 0.368 0.605 NA 0.889 #> 3 Outpatient 0.2 0.368 0.605 NA
#> # ℹ 1 more variable: colistin <dbl>
``` ```
``` r ``` r
# transform the antibiotic column to ATC codes: # transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc") out %>% set_ab_names(property = "atc")
#> # A tibble: 3 × 6 #> # A tibble: 3 × 6
@@ -393,6 +409,7 @@ This package is available [here on the official R network
R from CRAN by using the command: R from CRAN by using the command:
``` r ``` r
install.packages("AMR") install.packages("AMR")
``` ```
@@ -417,6 +434,7 @@ here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
To install the latest and unpublished beta version: To install the latest and unpublished beta version:
``` r ``` r
install.packages("AMR", repos = "beta.amr-for-r.org") install.packages("AMR", repos = "beta.amr-for-r.org")
# if this does not work, try to install directly from GitHub using the 'remotes' package: # if this does not work, try to install directly from GitHub using the 'remotes' package:

26
news/index.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -49,11 +49,17 @@
</div> </div>
<div class="section level2"> <div class="section level2">
<h2 class="pkg-version" data-toc-text="3.0.1.9052" id="amr-3019052">AMR 3.0.1.9052<a class="anchor" aria-label="anchor" href="#amr-3019052"></a></h2> <h2 class="pkg-version" data-toc-text="3.0.1.9053" id="amr-3019053">AMR 3.0.1.9053<a class="anchor" aria-label="anchor" href="#amr-3019053"></a></h2>
<p>This will become release v3.1.0, intended for launch end of May.</p>
<div class="section level4"> <div class="section level4">
<h4 id="new-3-0-1-9052">New<a class="anchor" aria-label="anchor" href="#new-3-0-1-9052"></a></h4> <h4 id="new-3-0-1-9053">New<a class="anchor" aria-label="anchor" href="#new-3-0-1-9053"></a></h4>
<ul><li>Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the <code>clinical_breakpoints</code> data set for usage in <code><a href="../reference/as.sir.html">as.sir()</a></code>. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations.</li> <ul><li>Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the <code>clinical_breakpoints</code> data set for usage in <code><a href="../reference/as.sir.html">as.sir()</a></code>. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations.</li>
<li>Integration with the <strong>tidymodels</strong> framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via <code>recipes</code> <li>Support for the <a href="https://future.futureverse.org" class="external-link"><code>future</code></a> package and its framework, as the previous implementation of parallel computing was slow
<ul><li>
<strong>Breaking change</strong>: <code><a href="../reference/as.sir.html">as.sir()</a></code> with <code>parallel = TRUE</code> now requires a non-sequential <code><a href="https://future.futureverse.org/reference/plan.html" class="external-link">future::plan()</a></code> to be active before the call — e.g., <code>future::plan(future::multisession)</code> — and throws an informative error if none is set.</li>
<li>New all-core usage setup: when the number of AB columns is smaller than the number of available cores, rows are now split into batches so all cores stay active (row-batch mode). Previously, a 6-column dataset on a 16-core machine would only use 6 cores; now all 16 are used, with each worker processing a smaller row slice (lower per-worker memory pressure and processing time)</li>
</ul></li>
<li>Integration with the <em>tidymodels</em> framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via <code>recipes</code>
<ul><li> <ul><li>
<code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> to transform <code>&lt;mic&gt;</code> columns with log2, and <code><a href="../reference/amr-tidymodels.html">step_sir_numeric()</a></code> to convert <code>&lt;sir&gt;</code> columns to numeric</li> <code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> to transform <code>&lt;mic&gt;</code> columns with log2, and <code><a href="../reference/amr-tidymodels.html">step_sir_numeric()</a></code> to convert <code>&lt;sir&gt;</code> columns to numeric</li>
<li>New <code>tidyselect</code> helpers: <li>New <code>tidyselect</code> helpers:
@@ -86,9 +92,8 @@
<li>Two new <code>NA</code> objects, <code>NA_ab_</code> and <code>NA_mo_</code>, analogous to base R’s <code>NA_character_</code> and <code>NA_integer_</code>, for use in pipelines that require typed missing values</li> <li>Two new <code>NA</code> objects, <code>NA_ab_</code> and <code>NA_mo_</code>, analogous to base R’s <code>NA_character_</code> and <code>NA_integer_</code>, for use in pipelines that require typed missing values</li>
</ul></div> </ul></div>
<div class="section level4"> <div class="section level4">
<h4 id="fixes-3-0-1-9052">Fixes<a class="anchor" aria-label="anchor" href="#fixes-3-0-1-9052"></a></h4> <h4 id="fixes-3-0-1-9053">Fixes<a class="anchor" aria-label="anchor" href="#fixes-3-0-1-9053"></a></h4>
<ul><li>Fixed multiple bugs in the <code>parallel = TRUE</code> mode of <code><a href="../reference/as.sir.html">as.sir()</a></code> for data frames</li> <ul><li>Fixed a bug in <code><a href="../reference/as.sir.html">as.sir()</a></code> where values that were purely numeric (e.g., <code>"1"</code>) and matched the broad SIR-matching regex would be incorrectly stripped of all content by the Unicode letter filter</li>
<li>Fixed a bug in <code><a href="../reference/as.sir.html">as.sir()</a></code> where values that were purely numeric (e.g., <code>"1"</code>) and matched the broad SIR-matching regex would be incorrectly stripped of all content by the Unicode letter filter</li>
<li>Fixed a bug in <code><a href="../reference/as.mic.html">as.mic()</a></code> where MIC values in scientific notation (e.g., <code>"1e-3"</code>) were incorrectly handled because the letter <code>e</code> was removed along with other Unicode letters; scientific notation <code>e</code> is now preserved</li> <li>Fixed a bug in <code><a href="../reference/as.mic.html">as.mic()</a></code> where MIC values in scientific notation (e.g., <code>"1e-3"</code>) were incorrectly handled because the letter <code>e</code> was removed along with other Unicode letters; scientific notation <code>e</code> is now preserved</li>
<li>Fixed a bug in <code><a href="../reference/as.ab.html">as.ab()</a></code> where certain AB codes containing “PH” or “TH” (such as <code>ETH</code>, <code>MTH</code>, <code>PHE</code>, <code>PHN</code>, <code>STH</code>, <code>THA</code>, <code>THI1</code>) would incorrectly return <code>NA</code> when combined in a vector with any untranslatable value (<a href="https://github.com/msberends/AMR/issues/245" class="external-link">#245</a>)</li> <li>Fixed a bug in <code><a href="../reference/as.ab.html">as.ab()</a></code> where certain AB codes containing “PH” or “TH” (such as <code>ETH</code>, <code>MTH</code>, <code>PHE</code>, <code>PHN</code>, <code>STH</code>, <code>THA</code>, <code>THI1</code>) would incorrectly return <code>NA</code> when combined in a vector with any untranslatable value (<a href="https://github.com/msberends/AMR/issues/245" class="external-link">#245</a>)</li>
<li>Fixed a bug in <code><a href="../reference/antibiogram.html">antibiogram()</a></code> for when no antimicrobials are set</li> <li>Fixed a bug in <code><a href="../reference/antibiogram.html">antibiogram()</a></code> for when no antimicrobials are set</li>
@@ -104,12 +109,10 @@
<li>Fixed BRMO classification by including bacterial complexes (<a href="https://github.com/msberends/AMR/issues/275" class="external-link">#275</a>)</li> <li>Fixed BRMO classification by including bacterial complexes (<a href="https://github.com/msberends/AMR/issues/275" class="external-link">#275</a>)</li>
<li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> for data frames silently deleting columns whose AB class was already <code>&lt;sir&gt;</code> when called a second time (re-running on already-converted data) (<a href="https://github.com/msberends/AMR/issues/278" class="external-link">#278</a>)</li> <li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> for data frames silently deleting columns whose AB class was already <code>&lt;sir&gt;</code> when called a second time (re-running on already-converted data) (<a href="https://github.com/msberends/AMR/issues/278" class="external-link">#278</a>)</li>
<li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> for data frames incorrectly treating metadata columns (e.g. <code>patient</code>, <code>ward</code>) as antibiotic columns when their names coincidentally matched an antibiotic code; column content is now validated against AMR data patterns before inclusion</li> <li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> for data frames incorrectly treating metadata columns (e.g. <code>patient</code>, <code>ward</code>) as antibiotic columns when their names coincidentally matched an antibiotic code; column content is now validated against AMR data patterns before inclusion</li>
<li>Improved parallel computing in <code><a href="../reference/as.sir.html">as.sir()</a></code>: when the number of AB columns is smaller than the number of available cores, rows are now split into batches so all cores stay active (row-batch mode). Previously, a 6-column dataset on a 16-core machine would only use 6 cores; now all 16 are used, with each worker processing a smaller row slice (lower per-worker memory pressure)</li> <li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> ignoring <code>info = FALSE</code> for columns with no breakpoints (e.g. cefoxitin against <em>E. coli</em>)</li>
<li>Fixed <code><a href="../reference/as.sir.html">as.sir()</a></code> ignoring <code>info = FALSE</code> for columns with no breakpoints (e.g. cefoxitin against <em>E. coli</em>): an operator-precedence bug (<code>&amp;&amp;</code>/<code>||</code>) caused the “Interpreting MIC values” intro message to fire unconditionally when <code>nrow(breakpoints) == 0</code>, regardless of <code>info</code>; the progress bar title was also not gated by <code>info</code>
</li>
</ul></div> </ul></div>
<div class="section level4"> <div class="section level4">
<h4 id="updates-3-0-1-9052">Updates<a class="anchor" aria-label="anchor" href="#updates-3-0-1-9052"></a></h4> <h4 id="updates-3-0-1-9053">Updates<a class="anchor" aria-label="anchor" href="#updates-3-0-1-9053"></a></h4>
<ul><li> <ul><li>
<code><a href="../reference/as.sir.html">as.sir()</a></code> with <code>reference_data</code>: custom guideline names now correctly classify values as R using EUCAST convention (<code>&gt; breakpoint_R</code> for MIC, <code>&lt; breakpoint_R</code> for disk); custom breakpoints with <code>host = NA</code> now serve as a host-agnostic fallback when no host-specific row matches (<a href="https://github.com/msberends/AMR/issues/239" class="external-link">#239</a>)</li> <code><a href="../reference/as.sir.html">as.sir()</a></code> with <code>reference_data</code>: custom guideline names now correctly classify values as R using EUCAST convention (<code>&gt; breakpoint_R</code> for MIC, <code>&lt; breakpoint_R</code> for disk); custom breakpoints with <code>host = NA</code> now serve as a host-agnostic fallback when no host-specific row matches (<a href="https://github.com/msberends/AMR/issues/239" class="external-link">#239</a>)</li>
<li>Extensive <code>cli</code> integration for better message handling and clickable links in messages and warnings (<a href="https://github.com/msberends/AMR/issues/191" class="external-link">#191</a>, <a href="https://github.com/msberends/AMR/issues/265" class="external-link">#265</a>)</li> <li>Extensive <code>cli</code> integration for better message handling and clickable links in messages and warnings (<a href="https://github.com/msberends/AMR/issues/191" class="external-link">#191</a>, <a href="https://github.com/msberends/AMR/issues/265" class="external-link">#265</a>)</li>
@@ -134,7 +137,6 @@
</ul></li> </ul></li>
<li> <li>
<code><a href="../reference/ab_property.html">ab_group()</a></code> now returns values consist with the AMR selectors (<a href="https://github.com/msberends/AMR/issues/246" class="external-link">#246</a>)</li> <code><a href="../reference/ab_property.html">ab_group()</a></code> now returns values consist with the AMR selectors (<a href="https://github.com/msberends/AMR/issues/246" class="external-link">#246</a>)</li>
<li>Added two new <code>NA</code> objects, <code>NA_ab_</code> and <code>NA_mo_</code>, analogous to base R’s <code>NA_character_</code> and <code>NA_integer_</code>, for use in pipelines that require typed missing values</li>
</ul></div> </ul></div>
</div> </div>
<div class="section level2"> <div class="section level2">

40
news/index.md
View File

@@ -1,6 +1,8 @@
# Changelog # Changelog
## AMR 3.0.1.9052 ## AMR 3.0.1.9053
This will become release v3.1.0, intended for launch end of May.
#### New #### New
@@ -10,7 +12,23 @@
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md). EUCAST 2026 [`as.sir()`](https://amr-for-r.org/reference/as.sir.md). EUCAST 2026
is now the new default guideline for all MIC and disk diffusion is now the new default guideline for all MIC and disk diffusion
interpretations. interpretations.
- Integration with the **tidymodels** framework to allow seamless use of - Support for the [`future`](https://future.futureverse.org) package and
its framework, as the previous implementation of parallel computing
was slow
- **Breaking change**:
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) with
`parallel = TRUE` now requires a non-sequential
[`future::plan()`](https://future.futureverse.org/reference/plan.html)
to be active before the call — e.g.,
`future::plan(future::multisession)` — and throws an informative
error if none is set.
- New all-core usage setup: when the number of AB columns is smaller
than the number of available cores, rows are now split into batches
so all cores stay active (row-batch mode). Previously, a 6-column
dataset on a 16-core machine would only use 6 cores; now all 16 are
used, with each worker processing a smaller row slice (lower
per-worker memory pressure and processing time)
- Integration with the *tidymodels* framework to allow seamless use of
SIR, MIC and disk data in modelling pipelines via `recipes` SIR, MIC and disk data in modelling pipelines via `recipes`
- [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md) - [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
to transform `<mic>` columns with log2, and to transform `<mic>` columns with log2, and
@@ -63,9 +81,6 @@
#### Fixes #### Fixes
- Fixed multiple bugs in the `parallel = TRUE` mode of
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) for data
frames
- Fixed a bug in [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) - Fixed a bug in [`as.sir()`](https://amr-for-r.org/reference/as.sir.md)
where values that were purely numeric (e.g., `"1"`) and matched the where values that were purely numeric (e.g., `"1"`) and matched the
broad SIR-matching regex would be incorrectly stripped of all content broad SIR-matching regex would be incorrectly stripped of all content
@@ -111,19 +126,9 @@
as antibiotic columns when their names coincidentally matched an as antibiotic columns when their names coincidentally matched an
antibiotic code; column content is now validated against AMR data antibiotic code; column content is now validated against AMR data
patterns before inclusion patterns before inclusion
- Improved parallel computing in
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md): when the
number of AB columns is smaller than the number of available cores,
rows are now split into batches so all cores stay active (row-batch
mode). Previously, a 6-column dataset on a 16-core machine would only
use 6 cores; now all 16 are used, with each worker processing a
smaller row slice (lower per-worker memory pressure)
- Fixed [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) ignoring - Fixed [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) ignoring
`info = FALSE` for columns with no breakpoints (e.g. cefoxitin against `info = FALSE` for columns with no breakpoints (e.g. cefoxitin against
*E. coli*): an operator-precedence bug (`&&`/`||`) caused the *E. coli*)
“Interpreting MIC values” intro message to fire unconditionally when
`nrow(breakpoints) == 0`, regardless of `info`; the progress bar title
was also not gated by `info`
#### Updates #### Updates
@@ -184,9 +189,6 @@
- [`ab_group()`](https://amr-for-r.org/reference/ab_property.md) now - [`ab_group()`](https://amr-for-r.org/reference/ab_property.md) now
returns values consist with the AMR selectors returns values consist with the AMR selectors
([\#246](https://github.com/msberends/AMR/issues/246)) ([\#246](https://github.com/msberends/AMR/issues/246))
- Added two new `NA` objects, `NA_ab_` and `NA_mo_`, analogous to base
R’s `NA_character_` and `NA_integer_`, for use in pipelines that
require typed missing values
## AMR 3.0.1 ## AMR 3.0.1

4
pkgdown.yml
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@@ -1,4 +1,4 @@
pandoc: 3.1.11 pandoc: 3.8.3
pkgdown: 2.2.0 pkgdown: 2.2.0
pkgdown_sha: ~ pkgdown_sha: ~
articles: articles:
@@ -10,7 +10,7 @@ articles:
PCA: PCA.html PCA: PCA.html
WHONET: WHONET.html WHONET: WHONET.html
WISCA: WISCA.html WISCA: WISCA.html
last_built: 2026-04-25T14:24Z last_built: 2026-04-30T08:02Z
urls: urls:
reference: https://amr-for-r.org/reference reference: https://amr-for-r.org/reference
article: https://amr-for-r.org/articles article: https://amr-for-r.org/articles

2
reference/AMR-deprecated.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/AMR-options.html
View File

@@ -9,7 +9,7 @@ options(AMR_guideline = "CLSI")'><meta property="og:image" content="https://amr-
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/AMR.html
View File

@@ -21,7 +21,7 @@ The AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

1
reference/AMR.md
View File

@@ -57,6 +57,7 @@ Data." *Journal of Statistical Software*, *104*(3), 1-31.
A BibTeX entry for LaTeX users is: A BibTeX entry for LaTeX users is:
@Article{, @Article{,
title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data}, title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner}, author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},

2
reference/WHOCC.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/WHONET.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/ab_from_text.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/ab_property.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/add_custom_antimicrobials.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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2
reference/add_custom_microorganisms.html
View File

@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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22
reference/age.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -112,16 +112,16 @@
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</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> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 26 26.81918 0</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 26 26.83288 0</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 58 58.23562 31</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 2 1968-01-29 58 58.24932 31</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 60 60.38630 34</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 3 1965-12-05 60 60.40000 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 46 46.15068 19</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 4 1980-03-01 46 46.16438 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 76 76.47945 50</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 5 1949-11-01 76 76.49315 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 79 79.19178 52</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 6 1947-02-14 79 79.20548 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 86 86.17808 59</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 7 1940-02-19 86 86.19178 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 38 38.28767 11</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 8 1988-01-10 38 38.30137 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 28 28.66027 2</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 9 1997-08-27 28 28.67397 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 48 48.24384 21</span> <span class="r-out co"><span class="r-pr">#&gt;</span> 10 1978-01-26 48 48.25753 21</span>
</code></pre></div> </code></pre></div>
</div> </div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>

20
reference/age.md
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@@ -81,14 +81,14 @@ df$age_at_y2k <- age(df$birth_date, "2000-01-01")
df df
#> birth_date age age_exact age_at_y2k #> birth_date age age_exact age_at_y2k
#> 1 1999-06-30 26 26.81918 0 #> 1 1999-06-30 26 26.83288 0
#> 2 1968-01-29 58 58.23562 31 #> 2 1968-01-29 58 58.24932 31
#> 3 1965-12-05 60 60.38630 34 #> 3 1965-12-05 60 60.40000 34
#> 4 1980-03-01 46 46.15068 19 #> 4 1980-03-01 46 46.16438 19
#> 5 1949-11-01 76 76.47945 50 #> 5 1949-11-01 76 76.49315 50
#> 6 1947-02-14 79 79.19178 52 #> 6 1947-02-14 79 79.20548 52
#> 7 1940-02-19 86 86.17808 59 #> 7 1940-02-19 86 86.19178 59
#> 8 1988-01-10 38 38.28767 11 #> 8 1988-01-10 38 38.30137 11
#> 9 1997-08-27 28 28.66027 2 #> 9 1997-08-27 28 28.67397 2
#> 10 1978-01-26 48 48.24384 21 #> 10 1978-01-26 48 48.25753 21
``` ```

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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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2
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@@ -7,7 +7,7 @@
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2
reference/amr_course.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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38
reference/antibiogram.html
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@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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@@ -411,7 +411,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 10 × 7</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 10 × 7</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin</span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin</span>
<span class="r-out co"><span class="r-pr">#&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;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;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;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;chr&gt;</span> </span>
@@ -426,7 +426,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> S. hominis <span style="color: #BB0000;">NA</span> 92% (84-9… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 85% (74-9…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> S. hominis <span style="color: #BB0000;">NA</span> 92% (84-9… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 85% (74-9…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> S. pneumoniae 0% (0-3%,N… 0% (0-3%,… <span style="color: #BB0000;">NA</span> 0% (0-3%… <span style="color: #BB0000;">NA</span> 0% (0-3%,…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> S. pneumoniae 0% (0-3%,N… 0% (0-3%,… <span style="color: #BB0000;">NA</span> 0% (0-3%… <span style="color: #BB0000;">NA</span> 0% (0-3%,…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span> <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
<span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span> <span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
@@ -435,14 +435,14 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 5</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 5</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 </span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 </span>
<span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span> <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
<span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span>,</span></span> <span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
@@ -450,7 +450,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"name"</span></span></span> <span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"name"</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 5 × 3</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 5 × 3</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Imipenem Meropenem </span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Imipenem Meropenem </span>
<span class="r-out co"><span class="r-pr">#&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> <span class="r-out co"><span class="r-pr">#&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>
@@ -460,7 +460,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> Proteus mirabilis 94% (79-99%,N=32) <span style="color: #BB0000;">NA</span> </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span> Proteus mirabilis 94% (79-99%,N=32) <span style="color: #BB0000;">NA</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Combined antibiogram -------------------------------------------------</span></span></span> <span class="r-in"><span><span class="co"># Combined antibiogram -------------------------------------------------</span></span></span>
@@ -470,7 +470,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span></span> <span class="r-in"><span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"TZP"</span>, <span class="st">"TZP+TOB"</span>, <span class="st">"TZP+GEN"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span> <span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 4</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 4</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
<span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> </span>
@@ -480,7 +480,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Piperacillin/tazobactam + Gentamicin`,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Piperacillin/tazobactam + Gentamicin`,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ³​`Piperacillin/tazobactam + Tobramycin`</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ³​`Piperacillin/tazobactam + Tobramycin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># you can use any antimicrobial selector with `+` too:</span></span></span> <span class="r-in"><span><span class="co"># you can use any antimicrobial selector with `+` too:</span></span></span>
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span> <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
@@ -488,7 +488,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span> <span class="r-in"><span> mo_transform <span class="op">=</span> <span class="st">"gramstain"</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `ureidopenicillins()` using column <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `ureidopenicillins()` using column <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 4</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 4</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³</span>
<span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> </span>
@@ -498,7 +498,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Piperacillin/tazobactam + Gentamicin`,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ²​`Piperacillin/tazobactam + Gentamicin`,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ³​`Piperacillin/tazobactam + Tobramycin`</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ³​`Piperacillin/tazobactam + Tobramycin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># names of antimicrobials do not need to resemble columns exactly:</span></span></span> <span class="r-in"><span><span class="co"># names of antimicrobials do not need to resemble columns exactly:</span></span></span>
<span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span> <span class="r-in"><span><span class="fu">antibiogram</span><span class="op">(</span><span class="va">example_isolates</span>,</span></span>
@@ -507,14 +507,14 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span> <span class="r-in"><span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span></span>
<span class="r-in"><span> sep <span class="op">=</span> <span class="st">" &amp; "</span></span></span> <span class="r-in"><span> sep <span class="op">=</span> <span class="st">" &amp; "</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 3</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 3</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Ciprofloxacin `Ciprofloxacin &amp; Gentamicin`</span> <span class="r-out co"><span class="r-pr">#&gt;</span> Pathogen Ciprofloxacin `Ciprofloxacin &amp; Gentamicin`</span>
<span class="r-out co"><span class="r-pr">#&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> <span class="r-out co"><span class="r-pr">#&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>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694) </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847) </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847) </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Syndromic antibiogram ------------------------------------------------</span></span></span> <span class="r-in"><span><span class="co"># Syndromic antibiogram ------------------------------------------------</span></span></span>
@@ -527,7 +527,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 14 × 8</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 14 × 8</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem</span> <span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem</span>
<span class="r-out co"><span class="r-pr">#&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;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;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;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;chr&gt;</span> </span>
@@ -547,7 +547,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">14</span> ICU S. pneumo… 0% (0-1… 0% (0-12%… <span style="color: #BB0000;">NA</span> 0% (0-12… <span style="color: #BB0000;">NA</span> </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">14</span> ICU S. pneumo… 0% (0-1… 0% (0-12%… <span style="color: #BB0000;">NA</span> 0% (0-12… <span style="color: #BB0000;">NA</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: Tobramycin &lt;chr&gt;</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: Tobramycin &lt;chr&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># now define a data set with only E. coli</span></span></span> <span class="r-in"><span><span class="co"># now define a data set with only E. coli</span></span></span>
<span class="r-in"><span><span class="va">ex1</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Escherichia"</span><span class="op">)</span>, <span class="op">]</span></span></span> <span class="r-in"><span><span class="va">ex1</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="mo_property.html">mo_genus</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Escherichia"</span><span class="op">)</span>, <span class="op">]</span></span></span>
@@ -565,14 +565,14 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span> <span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span> <span class="r-msg co"><span class="r-pr">#&gt;</span> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 2 × 5</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 2 × 5</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: Non-WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina</span> <span class="r-out co"><span class="r-pr">#&gt;</span> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina</span>
<span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span> <span class="r-out co"><span class="r-pr">#&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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99…</span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99…</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># WISCA antibiogram ----------------------------------------------------</span></span></span> <span class="r-in"><span><span class="co"># WISCA antibiogram ----------------------------------------------------</span></span></span>
@@ -583,7 +583,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span>,</span></span> <span class="r-in"><span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span>,</span></span>
<span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span> <span class="r-in"><span> wisca <span class="op">=</span> <span class="cn">TRUE</span></span></span>
<span class="r-in"><span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An Antibiogram: 3 × 4</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># An antibiogram: 3 × 4</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: WISCA with 95% CI</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Type: WISCA with 95% CI</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span> <span class="r-out co"><span class="r-pr">#&gt;</span> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹</span>
<span class="r-out co"><span class="r-pr">#&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> <span class="r-out co"><span class="r-pr">#&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>
@@ -593,7 +593,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` &lt;chr&gt;</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or https://quarto.org, see ?antibiogram</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># or use it directly in R Markdown or Quarto, see ?antibiogram</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Print the output for R Markdown / Quarto -----------------------------</span></span></span> <span class="r-in"><span><span class="co"># Print the output for R Markdown / Quarto -----------------------------</span></span></span>

36
reference/antibiogram.md
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@@ -602,7 +602,7 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 10 × 7 #> # An antibiogram: 10 × 7
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin #> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
@@ -617,7 +617,7 @@ antibiogram(example_isolates,
#> 9 S. hominis NA 92% (84-9… NA NA NA 85% (74-9… #> 9 S. hominis NA 92% (84-9… NA NA NA 85% (74-9…
#> 10 S. pneumoniae 0% (0-3%,N… 0% (0-3%,… NA 0% (0-3%… NA 0% (0-3%,… #> 10 S. pneumoniae 0% (0-3%,N… 0% (0-3%,… NA 0% (0-3%… NA 0% (0-3%,…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = aminoglycosides(), antimicrobials = aminoglycosides(),
@@ -626,14 +626,14 @@ antibiogram(example_isolates,
) )
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
#> # An Antibiogram: 2 × 5 #> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen J01GB01 J01GB03 J01GB04 J01GB06 #> Pathogen J01GB01 J01GB03 J01GB04 J01GB06
#> <chr> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr> <chr>
#> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-… #> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-…
#> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%… #> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = carbapenems(), antimicrobials = carbapenems(),
@@ -641,7 +641,7 @@ antibiogram(example_isolates,
mo_transform = "name" mo_transform = "name"
) )
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 5 × 3 #> # An antibiogram: 5 × 3
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen Imipenem Meropenem #> Pathogen Imipenem Meropenem
#> <chr> <chr> <chr> #> <chr> <chr> <chr>
@@ -651,7 +651,7 @@ antibiogram(example_isolates,
#> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N… #> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N…
#> 5 Proteus mirabilis 94% (79-99%,N=32) NA #> 5 Proteus mirabilis 94% (79-99%,N=32) NA
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# Combined antibiogram ------------------------------------------------- # Combined antibiogram -------------------------------------------------
@@ -661,7 +661,7 @@ antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain" mo_transform = "gramstain"
) )
#> # An Antibiogram: 2 × 4 #> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr>
@@ -671,7 +671,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin` #> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# you can use any antimicrobial selector with `+` too: # you can use any antimicrobial selector with `+` too:
antibiogram(example_isolates, antibiogram(example_isolates,
@@ -679,7 +679,7 @@ antibiogram(example_isolates,
mo_transform = "gramstain" mo_transform = "gramstain"
) )
#> ℹ For `ureidopenicillins()` using column TZP (piperacillin/tazobactam) #> ℹ For `ureidopenicillins()` using column TZP (piperacillin/tazobactam)
#> # An Antibiogram: 2 × 4 #> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr>
@@ -689,7 +689,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`, #> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin` #> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# names of antimicrobials do not need to resemble columns exactly: # names of antimicrobials do not need to resemble columns exactly:
antibiogram(example_isolates, antibiogram(example_isolates,
@@ -698,14 +698,14 @@ antibiogram(example_isolates,
ab_transform = "name", ab_transform = "name",
sep = " & " sep = " & "
) )
#> # An Antibiogram: 2 × 3 #> # An antibiogram: 2 × 3
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin` #> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin`
#> <chr> <chr> <chr> #> <chr> <chr> <chr>
#> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694) #> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694)
#> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847) #> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847)
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# Syndromic antibiogram ------------------------------------------------ # Syndromic antibiogram ------------------------------------------------
@@ -718,7 +718,7 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 14 × 8 #> # An antibiogram: 14 × 8
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem #> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
@@ -738,7 +738,7 @@ antibiogram(example_isolates,
#> 14 ICU S. pneumo… 0% (0-1… 0% (0-12%… NA 0% (0-12… NA #> 14 ICU S. pneumo… 0% (0-1… 0% (0-12%… NA 0% (0-12… NA
#> # ℹ 1 more variable: Tobramycin <chr> #> # ℹ 1 more variable: Tobramycin <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# now define a data set with only E. coli # now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ] ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@@ -756,14 +756,14 @@ antibiogram(ex1,
) )
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin) #> (amikacin), and KAN (kanamycin)
#> # An Antibiogram: 2 × 5 #> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI #> # Type: Non-WISCA with 95% CI
#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina #> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina
#> <chr> <chr> <chr> <chr> <chr> #> <chr> <chr> <chr> <chr> <chr>
#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… #> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99…
#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99… #> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# WISCA antibiogram ---------------------------------------------------- # WISCA antibiogram ----------------------------------------------------
@@ -774,7 +774,7 @@ antibiogram(example_isolates,
syndromic_group = "ward", syndromic_group = "ward",
wisca = TRUE wisca = TRUE
) )
#> # An Antibiogram: 3 × 4 #> # An antibiogram: 3 × 4
#> # Type: WISCA with 95% CI #> # Type: WISCA with 95% CI
#> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹ #> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹
#> <chr> <chr> <chr> #> <chr> <chr> <chr>
@@ -784,7 +784,7 @@ antibiogram(example_isolates,
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin` #> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr> #> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram #> # or use it directly in R Markdown or Quarto, see ?antibiogram
# Print the output for R Markdown / Quarto ----------------------------- # Print the output for R Markdown / Quarto -----------------------------

2
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@@ -17,7 +17,7 @@ my_data_with_all_these_columns %&amp;gt;%
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
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@@ -9,7 +9,7 @@ The antibiotics data set has been renamed to antimicrobials. The old name will b
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/as.av.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/as.mic.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/as.mo.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

36
reference/as.sir.html
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@@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -111,7 +111,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<span> include_PKPD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_include_PKPD"</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span> <span> include_PKPD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_include_PKPD"</span>, <span class="cn">TRUE</span><span class="op">)</span>,</span>
<span> breakpoint_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_breakpoint_type"</span>, <span class="st">"human"</span><span class="op">)</span>, host <span class="op">=</span> <span class="cn">NULL</span>,</span> <span> breakpoint_type <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"AMR_breakpoint_type"</span>, <span class="st">"human"</span><span class="op">)</span>, host <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>,</span> <span> language <span class="op">=</span> <span class="fu"><a href="translate.html">get_AMR_locale</a></span><span class="op">(</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, info <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/interactive.html" class="external-link">interactive</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> parallel <span class="op">=</span> <span class="cn">FALSE</span>, max_cores <span class="op">=</span> <span class="op">-</span><span class="fl">1</span>, conserve_capped_values <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span> <span> parallel <span class="op">=</span> <span class="cn">FALSE</span>, conserve_capped_values <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
<span></span> <span></span>
<span><span class="fu">sir_interpretation_history</span><span class="op">(</span>clean <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> <span><span class="fu">sir_interpretation_history</span><span class="op">(</span>clean <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
</div> </div>
@@ -227,11 +227,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt> <dt id="arg-parallel">parallel<a class="anchor" aria-label="anchor" href="#arg-parallel"></a></dt>
<dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if parallel computing must be used, defaults to <code>FALSE</code>. The <code>parallel</code> package is part of base <span style="R">R</span> and no additional packages are required. On Unix/macOS with <span style="R">R</span> &gt;= 4.0.0, <code><a href="https://rdrr.io/r/parallel/mclapply.html" class="external-link">parallel::mclapply()</a></code> (fork-based) is used; on Windows and <span style="R">R</span> &lt; 4.0.0, <code><a href="https://rdrr.io/r/parallel/clusterApply.html" class="external-link">parallel::parLapply()</a></code> with a PSOCK cluster is used (requires the AMR package to be installed, not just loaded via <code>devtools::load_all()</code>). Parallelism distributes columns across cores; it is most beneficial when there are many antibiotic columns and a large number of rows.</p></dd> <dd><p>A <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate if parallel computing must be used, defaults to <code>FALSE</code>. Requires the <code><a href="https://future.apply.futureverse.org/reference/future_lapply.html" class="external-link">future.apply</a></code> package. <strong>A non-sequential <code><a href="https://future.futureverse.org/reference/plan.html" class="external-link">future::plan()</a></code> must already be active before setting <code>parallel = TRUE</code></strong> — for example, <code>future::plan(future::multisession)</code>. An error is thrown if <code>parallel = TRUE</code> is used without a plan set by the user. Parallelism distributes columns (and optionally row batches) across workers; it is most beneficial when there are many antibiotic columns and a large number of rows.</p></dd>
<dt id="arg-max-cores">max_cores<a class="anchor" aria-label="anchor" href="#arg-max-cores"></a></dt>
<dd><p>Maximum number of cores to use if <code>parallel = TRUE</code>. Use a negative value to subtract that number from the available number of cores, e.g. a value of <code>-2</code> on an 8-core machine means that at most 6 cores will be used. Defaults to <code>-1</code>. There will never be used more cores than variables to analyse. The available number of cores are detected using <code><a href="https://parallelly.futureverse.org/reference/availableCores.html" class="external-link">parallelly::availableCores()</a></code> if that package is installed, and base <span style="R">R</span>'s <code><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">parallel::detectCores()</a></code> otherwise.</p></dd>
<dt id="arg-clean">clean<a class="anchor" aria-label="anchor" href="#arg-clean"></a></dt> <dt id="arg-clean">clean<a class="anchor" aria-label="anchor" href="#arg-clean"></a></dt>
@@ -258,7 +254,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<span><span class="co"># for veterinary breakpoints, also set `host`:</span></span> <span><span class="co"># for veterinary breakpoints, also set `host`:</span></span>
<span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.mic</span>, <span class="va">as.sir</span>, host <span class="op">=</span> <span class="st">"column_with_animal_species"</span>, guideline <span class="op">=</span> <span class="st">"CLSI"</span><span class="op">)</span></span> <span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.mic</span>, <span class="va">as.sir</span>, host <span class="op">=</span> <span class="st">"column_with_animal_species"</span>, guideline <span class="op">=</span> <span class="st">"CLSI"</span><span class="op">)</span></span>
<span></span> <span></span>
<span><span class="co"># fast processing with parallel computing:</span></span> <span><span class="co"># fast processing with parallel computing (requires future.apply):</span></span>
<span><span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">your_data</span>, <span class="va">...</span>, parallel <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre><p></p></div></li> <span><span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">your_data</span>, <span class="va">...</span>, parallel <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre><p></p></div></li>
<li><p>Operators like "&lt;=" will be considered according to the <code>capped_mic_handling</code> setting. At default, an MIC value of e.g. "&gt;2" will return "NI" (non-interpretable) if the breakpoint is 4-8; the <em>true</em> MIC could be at either side of the breakpoint. This is to prevent that capped values from raw laboratory data would not be treated conservatively.</p></li> <li><p>Operators like "&lt;=" will be considered according to the <code>capped_mic_handling</code> setting. At default, an MIC value of e.g. "&gt;2" will return "NI" (non-interpretable) if the breakpoint is 4-8; the <em>true</em> MIC could be at either side of the breakpoint. This is to prevent that capped values from raw laboratory data would not be treated conservatively.</p></li>
<li><p><strong>Note:</strong> When using CLSI as the guideline, MIC values must be log2-based doubling dilutions. Values not in this format, will be automatically rounded up to the nearest log2 level as CLSI instructs, and a warning will be thrown.</p></li> <li><p><strong>Note:</strong> When using CLSI as the guideline, MIC values must be log2-based doubling dilutions. Values not in this format, will be automatically rounded up to the nearest log2 level as CLSI instructs, and a warning will be thrown.</p></li>
@@ -272,7 +268,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<span><span class="co"># for veterinary breakpoints, also set `host`:</span></span> <span><span class="co"># for veterinary breakpoints, also set `host`:</span></span>
<span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.disk</span>, <span class="va">as.sir</span>, host <span class="op">=</span> <span class="st">"column_with_animal_species"</span>, guideline <span class="op">=</span> <span class="st">"CLSI"</span><span class="op">)</span></span> <span><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.disk</span>, <span class="va">as.sir</span>, host <span class="op">=</span> <span class="st">"column_with_animal_species"</span>, guideline <span class="op">=</span> <span class="st">"CLSI"</span><span class="op">)</span></span>
<span></span> <span></span>
<span><span class="co"># fast processing with parallel computing:</span></span> <span><span class="co"># fast processing with parallel computing (requires future.apply):</span></span>
<span><span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">your_data</span>, <span class="va">...</span>, parallel <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre><p></p></div></li> <span><span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="va">your_data</span>, <span class="va">...</span>, parallel <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre><p></p></div></li>
</ul></li> </ul></li>
<li><p>For <strong>interpreting a complete data set</strong>, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running <code>as.sir(your_data)</code>.</p></li> <li><p>For <strong>interpreting a complete data set</strong>, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running <code>as.sir(your_data)</code>.</p></li>
@@ -424,29 +420,15 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 4 × 18</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 4 × 18</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index method ab_given mo_given host_given input_given</span> <span class="r-out co"><span class="r-pr">#&gt;</span> datetime index method ab_given mo_given host_given input_given</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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </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;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2026-04-25 <span style="color: #949494;">14:25:30</span> 1 MIC amoxicillin Escherich… human 8 </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2026-04-30 <span style="color: #949494;">08:03:38</span> 1 MIC amoxicillin Escherich… human 8 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2026-04-25 <span style="color: #949494;">14:25:30</span> 1 MIC cipro Escherich… human 0.256 </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2026-04-30 <span style="color: #949494;">08:03:38</span> 1 MIC cipro Escherich… human 0.256 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2026-04-25 <span style="color: #949494;">14:25:31</span> 1 DISK tobra Escherich… human 16 </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2026-04-30 <span style="color: #949494;">08:03:38</span> 1 DISK tobra Escherich… human 16 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2026-04-25 <span style="color: #949494;">14:25:31</span> 1 DISK genta Escherich… human 18 </span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span> 2026-04-30 <span style="color: #949494;">08:03:39</span> 1 DISK genta Escherich… human 18 </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 11 more variables: ab &lt;ab&gt;, mo &lt;mo&gt;, host &lt;chr&gt;, input &lt;chr&gt;,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 11 more variables: ab &lt;ab&gt;, mo &lt;mo&gt;, host &lt;chr&gt;, input &lt;chr&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># outcome &lt;sir&gt;, notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># outcome &lt;sir&gt;, notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># breakpoint_S_R &lt;chr&gt;, site &lt;chr&gt;</span></span> <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># breakpoint_S_R &lt;chr&gt;, site &lt;chr&gt;</span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># \donttest{</span></span></span> <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="co"># using parallel computing, which is available in base R:</span></span></span>
<span class="r-in"><span><span class="fu">as.sir</span><span class="op">(</span><span class="va">df_wide</span>, parallel <span class="op">=</span> <span class="cn">TRUE</span>, info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Run `sir_interpretation_history()` afterwards to retrieve a logbook with all</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> details of the breakpoint interpretations.</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Processing columns:</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #080808; background-color: #5FD7AF;"> DONE </span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BBBB;">ℹ</span> Run `sir_interpretation_history()` to retrieve a logbook with all details of</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> the breakpoint interpretations.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> microorganism amoxicillin cipro tobra genta ERY</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 Escherichia coli S I S S R</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span> <span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co">## Using dplyr -------------------------------------------------</span></span></span> <span class="r-in"><span><span class="co">## Using dplyr -------------------------------------------------</span></span></span>
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span> <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://dplyr.tidyverse.org" class="external-link">"dplyr"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>

60
reference/as.sir.md
View File

@@ -69,7 +69,7 @@ as.sir(x, ..., col_mo = NULL,
include_PKPD = getOption("AMR_include_PKPD", TRUE), include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL, breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,
language = get_AMR_locale(), verbose = FALSE, info = interactive(), language = get_AMR_locale(), verbose = FALSE, info = interactive(),
parallel = FALSE, max_cores = -1, conserve_capped_values = NULL) parallel = FALSE, conserve_capped_values = NULL)
sir_interpretation_history(clean = FALSE) sir_interpretation_history(clean = FALSE)
``` ```
@@ -348,28 +348,16 @@ disk diffusion diameters:
- parallel: - parallel:
A [logical](https://rdrr.io/r/base/logical.html) to indicate if A [logical](https://rdrr.io/r/base/logical.html) to indicate if
parallel computing must be used, defaults to `FALSE`. The `parallel` parallel computing must be used, defaults to `FALSE`. Requires the
package is part of base R and no additional packages are required. On [`future.apply`](https://future.apply.futureverse.org/reference/future_lapply.html)
Unix/macOS with R \>= 4.0.0, package. **A non-sequential
[`parallel::mclapply()`](https://rdrr.io/r/parallel/mclapply.html) [`future::plan()`](https://future.futureverse.org/reference/plan.html)
(fork-based) is used; on Windows and R \< 4.0.0, must already be active before setting `parallel = TRUE`** — for
[`parallel::parLapply()`](https://rdrr.io/r/parallel/clusterApply.html) example, `future::plan(future::multisession)`. An error is thrown if
with a PSOCK cluster is used (requires the AMR package to be `parallel = TRUE` is used without a plan set by the user. Parallelism
installed, not just loaded via `devtools::load_all()`). Parallelism distributes columns (and optionally row batches) across workers; it is
distributes columns across cores; it is most beneficial when there are most beneficial when there are many antibiotic columns and a large
many antibiotic columns and a large number of rows. number of rows.
- max_cores:
Maximum number of cores to use if `parallel = TRUE`. Use a negative
value to subtract that number from the available number of cores, e.g.
a value of `-2` on an 8-core machine means that at most 6 cores will
be used. Defaults to `-1`. There will never be used more cores than
variables to analyse. The available number of cores are detected using
[`parallelly::availableCores()`](https://parallelly.futureverse.org/reference/availableCores.html)
if that package is installed, and base R's
[`parallel::detectCores()`](https://rdrr.io/r/parallel/detectCores.html)
otherwise.
- clean: - clean:
@@ -425,7 +413,7 @@ The `as.sir()` function can work in four ways:
# for veterinary breakpoints, also set `host`: # for veterinary breakpoints, also set `host`:
your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI") your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")
# fast processing with parallel computing: # fast processing with parallel computing (requires future.apply):
as.sir(your_data, ..., parallel = TRUE) as.sir(your_data, ..., parallel = TRUE)
- Operators like "\<=" will be considered according to the - Operators like "\<=" will be considered according to the
@@ -458,7 +446,7 @@ The `as.sir()` function can work in four ways:
# for veterinary breakpoints, also set `host`: # for veterinary breakpoints, also set `host`:
your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI") your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")
# fast processing with parallel computing: # fast processing with parallel computing (requires future.apply):
as.sir(your_data, ..., parallel = TRUE) as.sir(your_data, ..., parallel = TRUE)
4. For **interpreting a complete data set**, with automatic 4. For **interpreting a complete data set**, with automatic
@@ -679,29 +667,15 @@ sir_interpretation_history()
#> # A tibble: 4 × 18 #> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given #> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr> #> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 2026-04-25 14:25:30 1 MIC amoxicillin Escherich… human 8 #> 1 2026-04-30 08:03:38 1 MIC amoxicillin Escherich… human 8
#> 2 2026-04-25 14:25:30 1 MIC cipro Escherich… human 0.256 #> 2 2026-04-30 08:03:38 1 MIC cipro Escherich… human 0.256
#> 3 2026-04-25 14:25:31 1 DISK tobra Escherich… human 16 #> 3 2026-04-30 08:03:38 1 DISK tobra Escherich… human 16
#> 4 2026-04-25 14:25:31 1 DISK genta Escherich… human 18 #> 4 2026-04-30 08:03:39 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>, #> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>, #> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>, site <chr> #> # breakpoint_S_R <chr>, site <chr>
# \donttest{ # \donttest{
# using parallel computing, which is available in base R:
as.sir(df_wide, parallel = TRUE, info = TRUE)
#> ℹ Run `sir_interpretation_history()` afterwards to retrieve a logbook with all
#> details of the breakpoint interpretations.
#>
#> Processing columns:
#>
#> DONE
#>
#> ℹ Run `sir_interpretation_history()` to retrieve a logbook with all details of
#> the breakpoint interpretations.
#> microorganism amoxicillin cipro tobra genta ERY
#> 1 Escherichia coli S I S S R
## Using dplyr ------------------------------------------------- ## Using dplyr -------------------------------------------------
if (require("dplyr")) { if (require("dplyr")) {

2
reference/atc_online.html
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@@ -7,7 +7,7 @@
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reference/av_from_text.html
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reference/av_property.html
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reference/availability.html
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reference/bug_drug_combinations.html
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@@ -7,7 +7,7 @@
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reference/clinical_breakpoints.html
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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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reference/count.html
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@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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reference/dosage.html
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reference/esbl_isolates.html
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@@ -7,7 +7,7 @@
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reference/example_isolates.html
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@@ -7,7 +7,7 @@
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reference/example_isolates_unclean.html
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@@ -7,7 +7,7 @@
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reference/export_ncbi_biosample.html
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@@ -7,7 +7,7 @@
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reference/first_isolate.html
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@@ -9,7 +9,7 @@
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42
reference/first_isolate.md
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@@ -209,27 +209,27 @@ subspecies).
All mentioned methods are covered in the `first_isolate()` function: All mentioned methods are covered in the `first_isolate()` function:
| | | | | |
|-------------------------------------------------|--------------------------------------------------------| |----|----|
| **Method** | **Function to apply** | | **Method** | **Function to apply** |
| **Isolate-based** | `first_isolate(x, method = "isolate-based")` | | **Isolate-based** | `first_isolate(x, method = "isolate-based")` |
| *(= all isolates)* | | | *(= all isolates)* | |
| | | | | |
| | | | | |
| **Patient-based** | `first_isolate(x, method = "patient-based")` | | **Patient-based** | `first_isolate(x, method = "patient-based")` |
| *(= first isolate per patient)* | | | *(= first isolate per patient)* | |
| | | | | |
| | | | | |
| **Episode-based** | `first_isolate(x, method = "episode-based")`, or: | | **Episode-based** | `first_isolate(x, method = "episode-based")`, or: |
| *(= first isolate per episode)* | | | *(= first isolate per episode)* | |
| \- 7-Day interval from initial isolate | \- `first_isolate(x, method = "e", episode_days = 7)` | | \- 7-Day interval from initial isolate | \- `first_isolate(x, method = "e", episode_days = 7)` |
| \- 30-Day interval from initial isolate | \- `first_isolate(x, method = "e", episode_days = 30)` | | \- 30-Day interval from initial isolate | \- `first_isolate(x, method = "e", episode_days = 30)` |
| | | | | |
| | | | | |
| **Phenotype-based** | `first_isolate(x, method = "phenotype-based")`, or: | | **Phenotype-based** | `first_isolate(x, method = "phenotype-based")`, or: |
| *(= first isolate per phenotype)* | | | *(= first isolate per phenotype)* | |
| \- Major difference in any antimicrobial result | \- `first_isolate(x, type = "points")` | | \- Major difference in any antimicrobial result | \- `first_isolate(x, type = "points")` |
| \- Any difference in key antimicrobial results | \- `first_isolate(x, type = "keyantimicrobials")` | | \- Any difference in key antimicrobial results | \- `first_isolate(x, type = "keyantimicrobials")` |
**Isolate-based** **Isolate-based**

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reference/g.test.html
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reference/get_episode.html
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@@ -7,7 +7,7 @@
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reference/ggplot_pca.html
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reference/ggplot_sir.html
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@@ -7,7 +7,7 @@
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@@ -7,7 +7,7 @@
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reference/index.html
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reference/interpretive_rules.html
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@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive
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reference/key_antimicrobials.html
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@@ -7,7 +7,7 @@
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2
reference/kurtosis.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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2
reference/like.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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2
reference/mdro.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
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2
reference/mean_amr_distance.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/microorganisms.codes.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/microorganisms.groups.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/microorganisms.html
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@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/mo_matching_score.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/mo_property.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/mo_source.html
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@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/pca.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/plot.html
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@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/proportion.html
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@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/random.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/resistance_predict.html
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@@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/skewness.html
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@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/top_n_microorganisms.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
reference/translate.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a> <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9052</small> <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9053</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation"> <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">

2
search.json
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