From cfc92693483dfaf6a72567c572d571e1a00d3c2f Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Sun, 13 Apr 2025 08:10:01 +0000
Subject: [PATCH] Built site for AMR@2.1.1.9237: 7c3320b
---
404.html | 4 ++--
LICENSE-text.html | 4 ++--
articles/AMR.html | 12 ++++++------
articles/AMR_for_Python.html | 4 ++--
articles/AMR_with_tidymodels.html | 10 +++++-----
articles/EUCAST.html | 4 ++--
articles/MDR.html | 4 ++--
articles/PCA.html | 4 ++--
articles/WHONET.html | 4 ++--
articles/datasets.html | 6 +++---
articles/index.html | 4 ++--
articles/welcome_to_AMR.html | 4 ++--
authors.html | 4 ++--
extra.css | 4 ++++
extra.js | 4 ++--
index.html | 6 +++---
news/index.html | 24 +++++++++++++-----------
pkgdown.yml | 2 +-
reference/AMR-deprecated.html | 8 ++++----
reference/AMR-options.html | 4 ++--
reference/AMR.html | 4 ++--
reference/WHOCC.html | 4 ++--
reference/WHONET.html | 4 ++--
reference/ab_from_text.html | 4 ++--
reference/ab_property.html | 4 ++--
reference/add_custom_antimicrobials.html | 4 ++--
reference/add_custom_microorganisms.html | 4 ++--
reference/age.html | 24 ++++++++++++------------
reference/age_groups.html | 4 ++--
reference/antibiogram.html | 4 ++--
reference/antimicrobial_selectors.html | 4 ++--
reference/antimicrobials.html | 4 ++--
reference/as.ab.html | 4 ++--
reference/as.av.html | 4 ++--
reference/as.disk.html | 4 ++--
reference/as.mic.html | 4 ++--
reference/as.mo.html | 4 ++--
reference/as.sir.html | 22 +++++++++++-----------
reference/atc_online.html | 4 ++--
reference/av_from_text.html | 4 ++--
reference/av_property.html | 4 ++--
reference/availability.html | 4 ++--
reference/bug_drug_combinations.html | 4 ++--
reference/clinical_breakpoints.html | 4 ++--
reference/count.html | 4 ++--
reference/custom_eucast_rules.html | 4 ++--
reference/dosage.html | 4 ++--
reference/eucast_rules.html | 4 ++--
reference/example_isolates.html | 4 ++--
reference/example_isolates_unclean.html | 4 ++--
reference/export_ncbi_biosample.html | 4 ++--
reference/first_isolate.html | 4 ++--
reference/g.test.html | 4 ++--
reference/get_episode.html | 4 ++--
reference/ggplot_pca.html | 4 ++--
reference/ggplot_sir.html | 4 ++--
reference/guess_ab_col.html | 4 ++--
reference/index.html | 10 +++++-----
reference/intrinsic_resistant.html | 4 ++--
reference/italicise_taxonomy.html | 4 ++--
reference/join.html | 4 ++--
reference/key_antimicrobials.html | 4 ++--
reference/kurtosis.html | 4 ++--
reference/like.html | 4 ++--
reference/mdro.html | 4 ++--
reference/mean_amr_distance.html | 4 ++--
reference/microorganisms.codes.html | 4 ++--
reference/microorganisms.groups.html | 4 ++--
reference/microorganisms.html | 4 ++--
reference/mo_matching_score.html | 4 ++--
reference/mo_property.html | 4 ++--
reference/mo_source.html | 4 ++--
reference/pca.html | 4 ++--
reference/plot.html | 4 ++--
reference/proportion.html | 4 ++--
reference/random.html | 4 ++--
reference/resistance_predict.html | 11 +++++++----
reference/skewness.html | 4 ++--
reference/top_n_microorganisms.html | 4 ++--
reference/translate.html | 4 ++--
search.json | 2 +-
81 files changed, 211 insertions(+), 202 deletions(-)
diff --git a/404.html b/404.html
index 7460bfce7..8648a3419 100644
--- a/404.html
+++ b/404.html
@@ -32,7 +32,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -91,7 +91,7 @@ Content not found. Please use links in the navbar.
diff --git a/LICENSE-text.html b/LICENSE-text.html
index d2d367da4..2be50853c 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -306,7 +306,7 @@ END OF TERMS AND CONDITIONS
diff --git a/articles/AMR.html b/articles/AMR.html
index dc66e0af4..3cea48cca 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -92,7 +92,7 @@
website update since they are based on randomly created values and the
page was written in R
Markdown . However, the methodology remains unchanged. This page was
-generated on 12 April 2025.
+generated on 13 April 2025.
Introduction
@@ -148,21 +148,21 @@ make the structure of your data generally look like this:
-2025-04-12
+2025-04-13
abcd
Escherichia coli
S
S
-2025-04-12
+2025-04-13
abcd
Escherichia coli
S
R
-2025-04-12
+2025-04-13
efgh
Escherichia coli
R
@@ -1384,7 +1384,7 @@ values:
diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index f0cd9b36c..6cb8537ae 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -647,7 +647,7 @@ Python.
diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html
index 23597ac70..8b87badb7 100644
--- a/articles/AMR_with_tidymodels.html
+++ b/articles/AMR_with_tidymodels.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -89,9 +89,9 @@
-This page was entirely written by our AMR for R
-Assistant , a ChatGPT manually-trained model able to answer any
-question about the AMR package.
+This page was entirely written by our AMR for R Assistant , a ChatGPT
+manually-trained model able to answer any question about the AMR
+package.
Antimicrobial resistance (AMR) is a global health crisis, and
understanding resistance patterns is crucial for managing effective
@@ -693,7 +693,7 @@ settings.
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index fc1efea79..ce802981e 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -324,7 +324,7 @@ reading, and is basically a form of imputation:
diff --git a/articles/MDR.html b/articles/MDR.html
index 8732cdae6..200f71384 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -393,7 +393,7 @@ Unique: 5
diff --git a/articles/PCA.html b/articles/PCA.html
index b27bb1809..a4d6fc6b8 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -235,7 +235,7 @@ automatically adds the right labels and even groups:
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 06edb8e10..a5f9131d4 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -325,7 +325,7 @@ using the included ggplot_sir()
diff --git a/articles/datasets.html b/articles/datasets.html
index b1abb05af..e979c75b5 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -81,7 +81,7 @@
diff --git a/articles/index.html b/articles/index.html
index 79040a733..35106c510 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -78,7 +78,7 @@
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index 3d665e00d..e46399bde 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -31,7 +31,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -193,7 +193,7 @@ included with the package.
diff --git a/authors.html b/authors.html
index f8878a703..ff8c0d998 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -202,7 +202,7 @@
diff --git a/extra.css b/extra.css
index 16b453753..e95ca41e9 100644
--- a/extra.css
+++ b/extra.css
@@ -131,9 +131,13 @@ input[type="search"] {
width: 150px;
}
blockquote {
+ font-style: italic;
padding: 1.25rem 1.25rem;
border-left: 1rem solid var(--amr-green-dark);
}
+p {
+ text-align: justify;
+}
/*
this shows on top of every sidebar to the right
diff --git a/extra.js b/extra.js
index 58d559c72..d45f5f7e8 100644
--- a/extra.js
+++ b/extra.js
@@ -31,7 +31,7 @@
$(document).ready(function() {
// add GPT assistant info
- $('aside').prepend('');
+ $('aside').prepend('');
// replace 'Developers' with 'Maintainers' on the main page, and "Contributors" on the Authors page
$(".developers h2").text("Maintainers");
@@ -62,7 +62,7 @@ $(document).ready(function() {
if (window.location.href.includes('AMR_for_Python')) {
$('body').addClass('amr-for-python'); /* to set colours in CSS */
$('img[src="../logo.svg"]').attr('src', '../logo_python.svg'); /* replace base logo */
- $('img[src="https://github.com/msberends/AMR/raw/main/pkgdown/assets/AMRforRGPT.svg"]').attr('src', 'https://github.com/msberends/AMR/raw/main/pkgdown/assets/AMRforRGPT_python.svg'); /* replace GPT logo */
+ $('img[src="https://amr-for-r.org/AMRforRGPT.svg"]').attr('src', 'https://amr-for-r.org/AMRforRGPT_python.svg'); /* replace GPT logo */
}
// add country flags
diff --git a/index.html b/index.html
index c53165f81..b042d07d8 100644
--- a/index.html
+++ b/index.html
@@ -34,7 +34,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -466,7 +466,7 @@
# NEW scale function: write out S/I/R in any of the 20 supported languages
# and set colourblind-friendly colours
scale_colour_sir ( )
-
+
Calculating resistance per group
@@ -772,7 +772,7 @@
diff --git a/news/index.html b/news/index.html
index 716a3494f..9b8e80c2e 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -49,20 +49,20 @@
-
AMR 2.1.1.9236
+
AMR 2.1.1.9237
(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using the instructions here .)
-
A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
+
A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)
This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the University of Prince Edward Island’s Atlantic Veterinary College , Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
-
Breaking
+
Breaking
Dataset antibiotics
has been renamed to antimicrobials
as the data set contains more than just antibiotics. Using antibiotics
will still work, but now returns a warning.
Removed all functions and references that used the deprecated rsi
class, which were all replaced with their sir
equivalents over two years ago.
-Function resistance_predict()
is now deprecated and will be removed in a future version. Use the tidymodels
framework instead, for which we wrote a basic introduction .
+Functions resistance_predict()
and sir_predict()
is now deprecated and will be removed in a future version. Use the tidymodels
framework instead, for which we wrote a basic introduction .
-
New
+
New
One Health implementation
Function as.sir()
now has extensive support for veterinary breakpoints from CLSI. Use breakpoint_type = "animal"
and set the host
argument to a variable that contains animal species names.
@@ -116,11 +116,12 @@
-
Changed
+
Changed
SIR interpretation
It is now possible to use column names for argument ab
, mo
, and uti
: as.sir(..., ab = "column1", mo = "column2", uti = "column3")
. This greatly improves the flexibility for users.
Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.
To get quantitative values, as.double()
on a sir
object will return 1 for S, 2 for SDD/I, and 3 for R (NI will become NA
). Other functions using sir
classes (e.g., summary()
) are updated to reflect the change to contain NI and SDD.
+Following CLSI interpretation rules, values outside the log2-dilution range will be rounded upwards to the nearest log2-level before interpretation. Only if using a CLSI guideline.
Combined MIC values (e.g., from CLSI) are now supported
The argument conserve_capped_values
in as.sir()
has been replaced with capped_mic_handling
, which allows greater flexibility in handling capped MIC values (<
, <=
, >
, >=
). The four available options ("standard"
, "strict"
, "relaxed"
, "inverse"
) provide full control over whether these values should be interpreted conservatively or ignored. Using conserve_capped_values
is now deprecated and returns a warning.
Added argument info
so silence all console messages
@@ -195,8 +196,9 @@
Added console colours support of sir
class for Positron
-
Other
-
Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way
+Other
+New website domain: https://amr-for-r.org ! The old links (all based on http://amr-for-r.org ) will remain to work.
+Added Dr. Larisse Bolton and Aislinn Cook as contributors for their fantastic implementation of WISCA in a mathematically solid way
Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations
Added Prof. Kat Holt, Dr. Jane Hawkey, and Dr. Natacha Couto as contributors for their many suggestions, ideas and bugfixes
Greatly improved vctrs
integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as dplyr
’s bind_rows()
, rowwise()
and c_across()
are now supported for e.g. columns of class mic
. Despite this, this AMR
package is still zero-dependent on any other package, including dplyr
and vctrs
.
@@ -204,7 +206,7 @@
Stopped support for SAS (.xpt
) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.
-
Older Versions
+
Older Versions
This changelog only contains changes from AMR v3.0 (March 2025) and later.
diff --git a/pkgdown.yml b/pkgdown.yml
index 97d9560e8..3155296a9 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
PCA: PCA.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
-last_built: 2025-04-12T15:54Z
+last_built: 2025-04-13T08:06Z
urls:
reference: https://amr-for-r.org/reference
article: https://amr-for-r.org/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 7e6a284e5..8be39c1f3 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -1,5 +1,5 @@
-Deprecated Functions — AMR-deprecated • AMR (for R)
+Deprecated Functions, Arguments, or Datasets — AMR-deprecated • AMR (for R)
Skip to contents
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -44,7 +44,7 @@
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 098e9bbcd..cc00e5d0c 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -105,7 +105,7 @@
diff --git a/reference/AMR.html b/reference/AMR.html
index 74e1e5d17..0d12da0bd 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -21,7 +21,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -152,7 +152,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index d9adae491..5cc6f7d6c 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -91,7 +91,7 @@
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 5e139aae5..de3fdf7fa 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -130,7 +130,7 @@
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index e57cd3baf..cc31a7071 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -201,7 +201,7 @@
diff --git a/reference/ab_property.html b/reference/ab_property.html
index c8556043d..083deb4d1 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -442,7 +442,7 @@
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 9ad2a3644..16ffa6b9f 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -213,7 +213,7 @@
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index c16027a6d..0574c139b 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -244,7 +244,7 @@
diff --git a/reference/age.html b/reference/age.html
index ce6dfc76a..91b64124c 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -112,16 +112,16 @@
df
#> birth_date age age_exact age_at_y2k
-#> 1 1999-06-30 25 25.78356 0
-#> 2 1968-01-29 57 57.20000 31
-#> 3 1965-12-05 59 59.35068 34
-#> 4 1980-03-01 45 45.11507 19
-#> 5 1949-11-01 75 75.44384 50
-#> 6 1947-02-14 78 78.15616 52
-#> 7 1940-02-19 85 85.14247 59
-#> 8 1988-01-10 37 37.25205 11
-#> 9 1997-08-27 27 27.62466 2
-#> 10 1978-01-26 47 47.20822 21
+#> 1 1999-06-30 25 25.78630 0
+#> 2 1968-01-29 57 57.20274 31
+#> 3 1965-12-05 59 59.35342 34
+#> 4 1980-03-01 45 45.11781 19
+#> 5 1949-11-01 75 75.44658 50
+#> 6 1947-02-14 78 78.15890 52
+#> 7 1940-02-19 85 85.14521 59
+#> 8 1988-01-10 37 37.25479 11
+#> 9 1997-08-27 27 27.62740 2
+#> 10 1978-01-26 47 47.21096 21
On this page
@@ -133,7 +133,7 @@
diff --git a/reference/age_groups.html b/reference/age_groups.html
index 813971ab5..6c2591856 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -160,7 +160,7 @@ The default is to split on young children (0-11), youth (12-24), young adults (2
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index 5d5221f28..86af74cb8 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -659,7 +659,7 @@ $$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$
diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html
index bde2fb938..38a0add25 100644
--- a/reference/antimicrobial_selectors.html
+++ b/reference/antimicrobial_selectors.html
@@ -17,7 +17,7 @@ my_data_with_all_these_columns %>%
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -843,7 +843,7 @@ my_data_with_all_these_columns %>%
diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html
index 8857aa669..9544b5cd5 100644
--- a/reference/antimicrobials.html
+++ b/reference/antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -180,7 +180,7 @@
diff --git a/reference/as.ab.html b/reference/as.ab.html
index 1c25f16ef..18511b0f7 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -220,7 +220,7 @@
diff --git a/reference/as.av.html b/reference/as.av.html
index ae3482709..c94172a80 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -178,7 +178,7 @@
diff --git a/reference/as.disk.html b/reference/as.disk.html
index a0c484cf9..56a87ed83 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -168,7 +168,7 @@
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 8e699fdc5..9c412769d 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -233,7 +233,7 @@
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 375d93e72..07584db04 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -333,7 +333,7 @@
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 98f8f5269..41480840c 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -842,16 +842,16 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
#> # A tibble: 65 × 17
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
-#> 1 2025-04-12 15:55:26 1 DISK ampicillin Strep pneu human 18
-#> 2 2025-04-12 15:55:26 1 DISK AMP Escherich… human 20
-#> 3 2025-04-12 15:55:26 1 DISK AMP Escherich… human 20
+#> 1 2025-04-13 08:06:49 1 DISK ampicillin Strep pneu human 18
+#> 2 2025-04-13 08:06:49 1 DISK AMP Escherich… human 20
+#> 3 2025-04-13 08:06:49 1 DISK AMP Escherich… human 20
#> 4 NA NA NA NA NA NA NA
-#> 5 2025-04-12 15:55:27 1 DISK GEN Escherich… human 18
-#> 6 2025-04-12 15:55:27 1 DISK TOB Escherich… human 16
-#> 7 2025-04-12 15:55:27 1 MIC AMX B_STRPT_P… human 2
-#> 8 2025-04-12 15:55:28 1 MIC AMX B_STRPT_P… human 0.01
-#> 9 2025-04-12 15:55:28 2 MIC AMX B_STRPT_P… human 2
-#> 10 2025-04-12 15:55:28 3 MIC AMX B_STRPT_P… human 4
+#> 5 2025-04-13 08:06:50 1 DISK GEN Escherich… human 18
+#> 6 2025-04-13 08:06:50 1 DISK TOB Escherich… human 16
+#> 7 2025-04-13 08:06:51 1 MIC AMX B_STRPT_P… human 2
+#> 8 2025-04-13 08:06:51 1 MIC AMX B_STRPT_P… human 0.01
+#> 9 2025-04-13 08:06:51 2 MIC AMX B_STRPT_P… human 2
+#> 10 2025-04-13 08:06:51 3 MIC AMX B_STRPT_P… human 4
#> # ℹ 55 more rows
#> # ℹ 10 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
@@ -959,7 +959,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 5d165a962..c8303b3c4 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -151,7 +151,7 @@
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 4874625e2..77f265e43 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -140,7 +140,7 @@
diff --git a/reference/av_property.html b/reference/av_property.html
index a4ede058e..fd3ed761b 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -267,7 +267,7 @@
diff --git a/reference/availability.html b/reference/availability.html
index 5394bff99..c43c9995b 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -189,7 +189,7 @@
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index b0f8582e0..496e814fe 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -246,7 +246,7 @@
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index aa3d896eb..8a3345889 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."> AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -170,7 +170,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values.">
diff --git a/reference/count.html b/reference/count.html
index 79b1f833a..18fdcf7c2 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -272,7 +272,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 9ddc3a2e4..5708f8bbf 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -240,7 +240,7 @@
diff --git a/reference/dosage.html b/reference/dosage.html
index 8146aba3b..4719244e7 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -108,7 +108,7 @@
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index c74827145..cc0885c97 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -302,7 +302,7 @@ Leclercq et al. EUCAST expert rules in antimicrobial susceptibility test
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 640a81b05..d88fedfd3 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -111,7 +111,7 @@
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 14a14e813..2a35fedeb 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -103,7 +103,7 @@
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index 41e8b110c..bbdbb4033 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -86,7 +86,7 @@
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 5747231aa..60831f3fb 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -387,7 +387,7 @@
diff --git a/reference/g.test.html b/reference/g.test.html
index 9227250de..5e8175d8a 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -232,7 +232,7 @@
diff --git a/reference/get_episode.html b/reference/get_episode.html
index e61ba957f..2e079eada 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -319,7 +319,7 @@
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index d17ac6c20..3b3ffd608 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -245,7 +245,7 @@
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index b250f85a4..3be1ccba2 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -298,7 +298,7 @@
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 00c6fb8ab..3eba38dba 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -132,7 +132,7 @@
diff --git a/reference/index.html b/reference/index.html
index a585ab6a4..2c7a00baf 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -529,9 +529,9 @@
Skewness of the Sample
-
Other: deprecated functions
+
Other: deprecated functions/arguments/datasets
-
These functions are deprecated, meaning that they will still work but show a warning that they will be removed in a future version.
+
These objects are deprecated, meaning that they will still work but show a warning that they will be removed in a future version.
@@ -555,7 +555,7 @@
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 6def8cf90..070424fd8 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -106,7 +106,7 @@
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 0220d53a4..5c303a946 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -99,7 +99,7 @@
diff --git a/reference/join.html b/reference/join.html
index 25870141d..4bd1a2435 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -191,7 +191,7 @@
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index f2aa47c44..d5d1342a7 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -204,7 +204,7 @@
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 89b9af5df..966081c43 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -105,7 +105,7 @@
diff --git a/reference/like.html b/reference/like.html
index 0deadb5d6..0ee44820a 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -190,7 +190,7 @@
diff --git a/reference/mdro.html b/reference/mdro.html
index 009af6617..e9dd8369b 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -295,7 +295,7 @@ Ordered facto
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index fcf733c45..2d0321cba 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -235,7 +235,7 @@
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 1164880a9..2f5ca6d40 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -188,7 +188,7 @@
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 859d72de9..c666d49b1 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -117,7 +117,7 @@
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index b50f8dcd5..628ec0243 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -181,7 +181,7 @@ Public Health Information Network Vocabulary Access and Distribution System (PHI
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 1dbea2ce2..366fe5eae 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -152,7 +152,7 @@
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 061f24a49..e7a5aa60e 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -621,7 +621,7 @@
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 0aa730597..879fb5b30 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -149,7 +149,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
diff --git a/reference/pca.html b/reference/pca.html
index bbb23ca5a..9b141e420 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -184,7 +184,7 @@
diff --git a/reference/plot.html b/reference/plot.html
index d326338fb..4f1099f0b 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -479,7 +479,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
diff --git a/reference/proportion.html b/reference/proportion.html
index b1a7b51a9..cce777db1 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -411,7 +411,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
diff --git a/reference/random.html b/reference/random.html
index 7a1e4ab6b..eb2c17acc 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -149,7 +149,7 @@
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 7a931ec83..ac57c0a8f 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -1,5 +1,7 @@
-Predict Antimicrobial Resistance — resistance_predict • AMR (for R)
+Predict Antimicrobial Resistance — resistance_predict • AMR (for R)
Skip to contents
@@ -7,7 +9,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -50,7 +52,8 @@
-
Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns se_min
and se_max
. See Examples for a real live example.
+
Create a prediction model to predict antimicrobial resistance for the next years. Standard errors (SE) will be returned as columns se_min
and se_max
. See Examples for a real live example.
+
NOTE: These functions are deprecated and will be removed in a future version. Use the AMR package combined with the tidymodels framework instead, for which we have written a basic and short introduction on our website .
@@ -248,7 +251,7 @@
diff --git a/reference/skewness.html b/reference/skewness.html
index d0c995d3b..6ada39b04 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -102,7 +102,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index 924b32df5..dbe82768d 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -183,7 +183,7 @@
diff --git a/reference/translate.html b/reference/translate.html
index f1fe152ca..7f696baf5 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
AMR (for R)
- 2.1.1.9236
+ 2.1.1.9237
@@ -149,7 +149,7 @@
diff --git a/search.json b/search.json
index 6131eff35..591711b13 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the microorganisms.codes data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> mo_uncertainties() to review these uncertainties, or use #> add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella #> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), #> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii #> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a #> microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,724 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,714 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2724 Length:2724 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-07 #> Mode :character Mode :character Median :2015-06-03 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-11 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :41.6% (n=1133) %S :52.6% (n=1432) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) #> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=176) %I : 3.0% (n=82) #> %R :41.0% (n=1117) %R :36.0% (n=981) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1854 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,714 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 981 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: 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 , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #> #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #> all the details of the breakpoint interpretations. #> #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE #> • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'. my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 16.000 R #> 2 0.005 S #> 3 1.000 R #> 4 >=256.000 R #> 5 2.000 R #> 6 0.025 S #> 7 16.000 R #> 8 0.025 S #> 9 0.500 I #> 10 0.005 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: 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 , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modeling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.00e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.00e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN