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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>
<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">

2
reference/AMR-options.html
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@@ -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>
<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">

2
reference/AMR.html
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@@ -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>
<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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1
reference/AMR.md
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@@ -57,6 +57,7 @@ Data." *Journal of Statistical Software*, *104*(3), 1-31.
A BibTeX entry for LaTeX users is:
@Article{,
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},

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>
<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">

2
reference/WHONET.html
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@@ -7,7 +7,7 @@
<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">

2
reference/ab_from_text.html
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@@ -7,7 +7,7 @@
<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">

2
reference/ab_property.html
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@@ -7,7 +7,7 @@
<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">

2
reference/add_custom_antimicrobials.html
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@@ -7,7 +7,7 @@
<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">

2
reference/add_custom_microorganisms.html
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@@ -7,7 +7,7 @@
<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">

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>
<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">
@@ -112,16 +112,16 @@
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1999-06-30 26 26.81918 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> 3 1965-12-05 60 60.38630 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> 5 1949-11-01 76 76.47945 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> 7 1940-02-19 86 86.17808 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> 9 1997-08-27 28 28.66027 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> 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 58.24932 31</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 46.16438 19</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 79.20548 52</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 38.30137 11</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 48.25753 21</span>
</code></pre></div>
</div>
</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
#> birth_date age age_exact age_at_y2k
#> 1 1999-06-30 26 26.81918 0
#> 2 1968-01-29 58 58.23562 31
#> 3 1965-12-05 60 60.38630 34
#> 4 1980-03-01 46 46.15068 19
#> 5 1949-11-01 76 76.47945 50
#> 6 1947-02-14 79 79.19178 52
#> 7 1940-02-19 86 86.17808 59
#> 8 1988-01-10 38 38.28767 11
#> 9 1997-08-27 28 28.66027 2
#> 10 1978-01-26 48 48.24384 21
#> 1 1999-06-30 26 26.83288 0
#> 2 1968-01-29 58 58.24932 31
#> 3 1965-12-05 60 60.40000 34
#> 4 1980-03-01 46 46.16438 19
#> 5 1949-11-01 76 76.49315 50
#> 6 1947-02-14 79 79.20548 52
#> 7 1940-02-19 86 86.19178 59
#> 8 1988-01-10 38 38.30137 11
#> 9 1997-08-27 28 28.67397 2
#> 10 1978-01-26 48 48.25753 21
```

2
reference/age_groups.html
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@@ -7,7 +7,7 @@
<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">

2
reference/amr-tidymodels.html
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@@ -7,7 +7,7 @@
<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">

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>
<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">

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>
<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">
@@ -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> (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-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> 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>
@@ -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;">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;"># 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 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>
@@ -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-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-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> 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: #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: #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 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>
@@ -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><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-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> 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>
@@ -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;">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;"># 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"># 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> 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-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> 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>
@@ -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;"># 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;"># 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 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>
@@ -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><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-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> 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>
@@ -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;"># 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;"># 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 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>
@@ -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> 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-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> 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: #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: #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"># 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> (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-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> `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>
@@ -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: #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;"># 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 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>
@@ -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-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-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> `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: #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: #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"># 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> 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-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> `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>
@@ -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 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;"># 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"># Print the output for R Markdown / Quarto -----------------------------</span></span></span>

36
reference/antibiogram.md
View File

@@ -602,7 +602,7 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 10 × 7
#> # An antibiogram: 10 × 7
#> # Type: Non-WISCA with 95% CI
#> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin
#> <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…
#> 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,
#> # 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,
antimicrobials = aminoglycosides(),
@@ -626,14 +626,14 @@ antibiogram(example_isolates,
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> # An Antibiogram: 2 × 5
#> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
#> Pathogen J01GB01 J01GB03 J01GB04 J01GB06
#> <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-…
#> 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,
#> # 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,
antimicrobials = carbapenems(),
@@ -641,7 +641,7 @@ antibiogram(example_isolates,
mo_transform = "name"
)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 5 × 3
#> # An antibiogram: 5 × 3
#> # Type: Non-WISCA with 95% CI
#> Pathogen Imipenem Meropenem
#> <chr> <chr> <chr>
@@ -651,7 +651,7 @@ antibiogram(example_isolates,
#> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N…
#> 5 Proteus mirabilis 94% (79-99%,N=32) NA
#> # 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 -------------------------------------------------
@@ -661,7 +661,7 @@ antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain"
)
#> # An Antibiogram: 2 × 4
#> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr>
@@ -671,7 +671,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # 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:
antibiogram(example_isolates,
@@ -679,7 +679,7 @@ antibiogram(example_isolates,
mo_transform = "gramstain"
)
#> ℹ For `ureidopenicillins()` using column TZP (piperacillin/tazobactam)
#> # An Antibiogram: 2 × 4
#> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr>
@@ -689,7 +689,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # 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:
antibiogram(example_isolates,
@@ -698,14 +698,14 @@ antibiogram(example_isolates,
ab_transform = "name",
sep = " & "
)
#> # An Antibiogram: 2 × 3
#> # An antibiogram: 2 × 3
#> # Type: Non-WISCA with 95% CI
#> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin`
#> <chr> <chr> <chr>
#> 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)
#> # 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 ------------------------------------------------
@@ -718,7 +718,7 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An Antibiogram: 14 × 8
#> # An antibiogram: 14 × 8
#> # Type: Non-WISCA with 95% CI
#> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem
#> <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
#> # ℹ 1 more variable: Tobramycin <chr>
#> # 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
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@@ -756,14 +756,14 @@ antibiogram(ex1,
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> # An Antibiogram: 2 × 5
#> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina
#> <chr> <chr> <chr> <chr> <chr>
#> 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…
#> # 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 ----------------------------------------------------
@@ -774,7 +774,7 @@ antibiogram(example_isolates,
syndromic_group = "ward",
wisca = TRUE
)
#> # An Antibiogram: 3 × 4
#> # An antibiogram: 3 × 4
#> # Type: WISCA with 95% CI
#> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹
#> <chr> <chr> <chr>
@@ -784,7 +784,7 @@ antibiogram(example_isolates,
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # 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 -----------------------------

2
reference/antimicrobial_selectors.html
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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>
<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">

2
reference/antimicrobials.html
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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>
<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">

2
reference/as.ab.html
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@@ -7,7 +7,7 @@
<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">

2
reference/as.av.html
View File

@@ -7,7 +7,7 @@
<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">

2
reference/as.disk.html
View File

@@ -7,7 +7,7 @@
<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">

2
reference/as.mic.html
View File

@@ -7,7 +7,7 @@
<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">

2
reference/as.mo.html
View File

@@ -7,7 +7,7 @@
<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">

36
reference/as.sir.html
View File

@@ -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>
<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">
@@ -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> 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> 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 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>
@@ -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>
<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>
<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>
<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-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="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 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>
<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>
@@ -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="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 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>
</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>
@@ -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> 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: #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;">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;">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;">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;">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> 2026-04-30 <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> 2026-04-30 <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> 2026-04-30 <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;"># 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-in"><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 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>

60
reference/as.sir.md
View File

@@ -69,7 +69,7 @@ as.sir(x, ..., col_mo = NULL,
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"), host = NULL,
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)
```
@@ -348,28 +348,16 @@ disk diffusion diameters:
- parallel:
A [logical](https://rdrr.io/r/base/logical.html) to indicate if
parallel computing must be used, defaults to `FALSE`. The `parallel`
package is part of base R and no additional packages are required. On
Unix/macOS with R \>= 4.0.0,
[`parallel::mclapply()`](https://rdrr.io/r/parallel/mclapply.html)
(fork-based) is used; on Windows and R \< 4.0.0,
[`parallel::parLapply()`](https://rdrr.io/r/parallel/clusterApply.html)
with a PSOCK cluster is used (requires the AMR package to be
installed, not just loaded via `devtools::load_all()`). Parallelism
distributes columns across cores; it is most beneficial when there are
many antibiotic columns and a large 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.
parallel computing must be used, defaults to `FALSE`. Requires the
[`future.apply`](https://future.apply.futureverse.org/reference/future_lapply.html)
package. **A non-sequential
[`future::plan()`](https://future.futureverse.org/reference/plan.html)
must already be active before setting `parallel = TRUE`** — for
example, `future::plan(future::multisession)`. An error is thrown if
`parallel = TRUE` 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.
- clean:
@@ -425,7 +413,7 @@ The `as.sir()` function can work in four ways:
# for veterinary breakpoints, also set `host`:
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)
- 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`:
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)
4. For **interpreting a complete data set**, with automatic
@@ -679,29 +667,15 @@ sir_interpretation_history()
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 2026-04-25 14:25:30 1 MIC amoxicillin Escherich… human 8
#> 2 2026-04-25 14:25:30 1 MIC cipro Escherich… human 0.256
#> 3 2026-04-25 14:25:31 1 DISK tobra Escherich… human 16
#> 4 2026-04-25 14:25:31 1 DISK genta Escherich… human 18
#> 1 2026-04-30 08:03:38 1 MIC amoxicillin Escherich… human 8
#> 2 2026-04-30 08:03:38 1 MIC cipro Escherich… human 0.256
#> 3 2026-04-30 08:03:38 1 DISK tobra Escherich… human 16
#> 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>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>, site <chr>
# \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 -------------------------------------------------
if (require("dplyr")) {

2
reference/atc_online.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/av_from_text.html
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@@ -7,7 +7,7 @@
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reference/av_property.html
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@@ -7,7 +7,7 @@
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reference/availability.html
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@@ -7,7 +7,7 @@
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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/custom_eucast_rules.html
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@@ -7,7 +7,7 @@
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reference/custom_mdro_guideline.html
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@@ -7,7 +7,7 @@
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reference/dosage.html
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@@ -7,7 +7,7 @@
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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:
| | |
|-------------------------------------------------|--------------------------------------------------------|
| **Method** | **Function to apply** |
| **Isolate-based** | `first_isolate(x, method = "isolate-based")` |
| *(= all isolates)* | |
| | |
| | |
| **Patient-based** | `first_isolate(x, method = "patient-based")` |
| *(= first isolate per patient)* | |
| | |
| | |
| **Episode-based** | `first_isolate(x, method = "episode-based")`, or: |
| *(= first isolate per episode)* | |
| \- 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)` |
| | |
| | |
| **Phenotype-based** | `first_isolate(x, method = "phenotype-based")`, or: |
| *(= first isolate per phenotype)* | |
| \- Major difference in any antimicrobial result | \- `first_isolate(x, type = "points")` |
| \- Any difference in key antimicrobial results | \- `first_isolate(x, type = "keyantimicrobials")` |
| | |
|----|----|
| **Method** | **Function to apply** |
| **Isolate-based** | `first_isolate(x, method = "isolate-based")` |
| *(= all isolates)* | |
| | |
| | |
| **Patient-based** | `first_isolate(x, method = "patient-based")` |
| *(= first isolate per patient)* | |
| | |
| | |
| **Episode-based** | `first_isolate(x, method = "episode-based")`, or: |
| *(= first isolate per episode)* | |
| \- 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)` |
| | |
| | |
| **Phenotype-based** | `first_isolate(x, method = "phenotype-based")`, or: |
| *(= first isolate per phenotype)* | |
| \- Major difference in any antimicrobial result | \- `first_isolate(x, type = "points")` |
| \- Any difference in key antimicrobial results | \- `first_isolate(x, type = "keyantimicrobials")` |
**Isolate-based**

2
reference/g.test.html
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@@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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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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@@ -7,7 +7,7 @@
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reference/ggplot_sir.html
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@@ -7,7 +7,7 @@
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reference/guess_ab_col.html
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@@ -7,7 +7,7 @@
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reference/index.html
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@@ -7,7 +7,7 @@
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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/intrinsic_resistant.html
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@@ -7,7 +7,7 @@
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reference/italicise_taxonomy.html
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reference/join.html
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@@ -7,7 +7,7 @@
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reference/key_antimicrobials.html
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@@ -7,7 +7,7 @@
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@@ -7,7 +7,7 @@
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reference/like.html
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@@ -7,7 +7,7 @@
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reference/mdro.html
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@@ -7,7 +7,7 @@
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reference/mean_amr_distance.html
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@@ -7,7 +7,7 @@
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reference/microorganisms.groups.html
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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
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reference/mo_matching_score.html
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@@ -7,7 +7,7 @@
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reference/mo_property.html
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@@ -7,7 +7,7 @@
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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
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reference/pca.html
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@@ -7,7 +7,7 @@
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reference/plot.html
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@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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reference/proportion.html
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@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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reference/random.html
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@@ -7,7 +7,7 @@
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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
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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
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reference/top_n_microorganisms.html
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reference/translate.html
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