The default AMR Python package uses the latest stable
+version of the AMR R package, published on CRAN. After
+running pip install AMR, import it as usual:
+
import AMR
+
+AMR.example_isolates
+
+
+
Development Version (GitHub)
+
+
To use the latest development version of the AMR R
+package (sourced directly from GitHub), import the beta
+sub-package and alias it as AMR:
+
import AMR.beta as AMR
+
+AMR.example_isolates
+
Aliasing with as AMR keeps all downstream code identical
+to the stable import. Switching between the stable release and the
+development version requires changing only the import line — nothing
+else in your script needs to change.
+
+
+
+
SIR Classification with as_sir()
+
+
+
Using enforce_method
+
+
The as_sir() function in R uses S3 method dispatch to
+select the correct calculation method based on the input class:
+<mic> for MIC values and <disk>
+for disk diffusion values. Because Python objects do not carry R class
+attributes through the rpy2 bridge, this automatic dispatch
+may not resolve correctly.
+
To explicitly specify the input type, use the
+enforce_method argument:
+
# Treat the column as MIC values — maps to R's as.sir.mic()
+AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
+
+# Treat the column as disk diffusion values — maps to R's as.sir.disk()
+AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
+
Without enforce_method, R falls back to class-based
+dispatch on the raw Python input, which may fail or return unexpected
+results. Always supply enforce_method when calling
+as_sir() from Python.
+
+
+
Conclusion
With the AMR Python package, Python users can now
diff --git a/articles/AMR_for_Python.md b/articles/AMR_for_Python.md
index 2ee907a0c..0d5f77ac8 100644
--- a/articles/AMR_for_Python.md
+++ b/articles/AMR_for_Python.md
@@ -200,6 +200,61 @@ AMR.antimicrobials
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
+## Installation Channels
+
+### Stable Release (CRAN)
+
+The default `AMR` Python package uses the latest stable version of the
+`AMR` R package, published on CRAN. After running `pip install AMR`,
+import it as usual:
+
+``` python
+import AMR
+
+AMR.example_isolates
+```
+
+### Development Version (GitHub)
+
+To use the latest development version of the `AMR` R package (sourced
+directly from GitHub), import the `beta` sub-package and alias it as
+`AMR`:
+
+``` python
+import AMR.beta as AMR
+
+AMR.example_isolates
+```
+
+Aliasing with `as AMR` keeps all downstream code identical to the stable
+import. Switching between the stable release and the development version
+requires changing only the import line — nothing else in your script
+needs to change.
+
+## SIR Classification with `as_sir()`
+
+### Using `enforce_method`
+
+The `as_sir()` function in R uses S3 method dispatch to select the
+correct calculation method based on the input class: `` for MIC
+values and `` for disk diffusion values. Because Python objects do
+not carry R class attributes through the `rpy2` bridge, this automatic
+dispatch may not resolve correctly.
+
+To explicitly specify the input type, use the `enforce_method` argument:
+
+``` python
+# Treat the column as MIC values — maps to R's as.sir.mic()
+AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")
+
+# Treat the column as disk diffusion values — maps to R's as.sir.disk()
+AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")
+```
+
+Without `enforce_method`, R falls back to class-based dispatch on the
+raw Python input, which may fail or return unexpected results. Always
+supply `enforce_method` when calling `as_sir()` from Python.
+
## Conclusion
With the `AMR` Python package, Python users can now effortlessly call R
diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html
index a8d14fbca..d42739f72 100644
--- a/articles/AMR_with_tidymodels.html
+++ b/articles/AMR_with_tidymodels.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 51b36208c..e9d5881be 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/PCA.html b/articles/PCA.html
index fbd2472d6..ff12452b5 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/WHONET.html b/articles/WHONET.html
index c4dfc54d1..1db52deea 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/WISCA.html b/articles/WISCA.html
index ebe8efb4b..c2899aaaa 100644
--- a/articles/WISCA.html
+++ b/articles/WISCA.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/datasets.html b/articles/datasets.html
index 567b4593b..757953635 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/articles/index.html b/articles/index.html
index b475b3c1b..e27d72c14 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/authors.html b/authors.html
index 4378d2c58..46882d905 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/index.html b/index.html
index 6b00f9300..5ec207686 100644
--- a/index.html
+++ b/index.html
@@ -33,7 +33,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/news/index.html b/news/index.html
index bb0bbc02c..ef489aba4 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
@@ -49,15 +49,15 @@
-
AMR 3.0.1.9075
+
AMR 3.0.1.9076
Planned as v3.1.0, end of June 2026.
-
Breaking Changes
+
Breaking Changes
The former kingdoms Bacteria and Archaea are now each divided into four kingdoms with new top-level domains ‘Bacteria’ and ‘Archaea’ (Göker and Oren, 2024, DOI: 10.1099/ijsem.0.006242). Following this, a new domain column in the microorganisms data set was added, and more importantly, mo_kingdom() now returns the formal kingdom (e.g. "Pseudomonadati" instead of "Bacteria"). Use mo_domain() for the old behaviour. For non-prokaryotic kingdoms (Fungi, Protozoa, etc.), kingdom and domain are identical.
Faster parallel computing via the future package for as.sir() and wisca(): a non-sequential plan (e.g. future::plan(future::multisession)) must be active before using parallel = TRUE.
-
New
+
New
EUCAST 2026 and CLSI 2026 breakpoints: over 5,700 new breakpoints added to the clinical_breakpoints data set; EUCAST 2026 is now the default for all MIC and disk diffusion interpretations
Wildtype/Non-wildtype (WT/NWT) output when using ECOFF-based interpretation, by setting breakpoint_type = "ECOFF" in as.sir(); WT/NWT results are fully supported in all resistance/susceptibility functions and plots (#254)
@@ -74,7 +74,7 @@
New wisca_plot() to assess the susceptibility and incidence distributions from the Monte Carlo simulations
On data frames: already-converted SIR columns no longer dropped on re-run (#278)
@@ -101,7 +101,7 @@
-
Updated
+
Updated
Taxonomic update for all microorganisms, now updated to June 2026
mo_kingdom() now returns the formal taxonomic kingdom; a one-time note per session explains the change when querying bacterial or archaeal records.
@@ -127,6 +127,8 @@
as.disk(): input validation is now more strict, rejecting values that are not recognisable as a numeric disk zone diameter
as.sir() gains an enforce_method argument ("auto", "mic", or "disk") to force the interpretation method when S3 class information is lost, e.g. when called from Python (#291)
+
+AMR for Python vignette: added sections on installation channels (stable CRAN vs. development GitHub via AMR.beta) and on using enforce_method in as_sir() from Python
diff --git a/news/index.md b/news/index.md
index 763b09dda..b7e5ce260 100644
--- a/news/index.md
+++ b/news/index.md
@@ -1,6 +1,6 @@
# Changelog
-## AMR 3.0.1.9075
+## AMR 3.0.1.9076
Planned as v3.1.0, end of June 2026.
@@ -180,6 +180,9 @@ Planned as v3.1.0, end of June 2026.
the interpretation method when S3 class information is lost, e.g. when
called from Python
([\#291](https://github.com/msberends/AMR/issues/291))
+- `AMR for Python` vignette: added sections on installation channels
+ (stable CRAN vs. development GitHub via `AMR.beta`) and on using
+ `enforce_method` in `as_sir()` from Python
## AMR 3.0.1
diff --git a/pkgdown.yml b/pkgdown.yml
index a721e7698..250c4f1db 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
PCA: PCA.html
WHONET: WHONET.html
WISCA: WISCA.html
-last_built: 2026-06-26T10:08Z
+last_built: 2026-06-26T13:07Z
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 47688d6d1..f5e5f24be 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index f6fd11f97..a9eb39780 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -9,7 +9,7 @@ options(AMR_guideline = "CLSI")'>AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/AMR.html b/reference/AMR.html
index 25c4bbe80..7798f4d3c 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -21,7 +21,7 @@ The AMR package is available in English, Arabic, Bengali, Chinese, Czech, Danish
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 4eb47821d..a8cad6c62 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/WHONET.html b/reference/WHONET.html
index b97682566..f72e39e41 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index fc72c2ffc..5bd16ff28 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/ab_property.html b/reference/ab_property.html
index f16e354d0..b6b4d5349 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index accf951ee..c25ad23cc 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 002b6ae4e..a17ff5842 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/age.html b/reference/age.html
index cd6270394..eceb4955e 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/age_groups.html b/reference/age_groups.html
index bfc5a4ead..cca9d60b8 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html
index d9f1994e8..adcdbff73 100644
--- a/reference/amr-tidymodels.html
+++ b/reference/amr-tidymodels.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/amr_course.html b/reference/amr_course.html
index 6b35b3d6b..becb4d8de 100644
--- a/reference/amr_course.html
+++ b/reference/amr_course.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index c6950b64a..83f510bea 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -13,7 +13,7 @@ All antibiogram types adhere to previously described approaches (see Source), an
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html
index 3cbcac38a..1035fcaac 100644
--- a/reference/antimicrobial_selectors.html
+++ b/reference/antimicrobial_selectors.html
@@ -17,7 +17,7 @@ my_data_with_all_these_columns %>%
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html
index 1b5824b59..205cd87dc 100644
--- a/reference/antimicrobials.html
+++ b/reference/antimicrobials.html
@@ -9,7 +9,7 @@ The antibiotics data set has been renamed to antimicrobials. The old name will b
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.ab.html b/reference/as.ab.html
index fad887481..682f7f12f 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.av.html b/reference/as.av.html
index 4e5dfc9b8..ad402ac84 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 5c5671d15..71a029fbd 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.mic.html b/reference/as.mic.html
index b59406ca6..844294d8f 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 089cd366e..afa38e351 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 7e0a30cd7..4b3c8ce4f 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
@@ -462,10 +462,10 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026,
#># A tibble: 4 × 18#> datetime index method ab_given mo_given host_given input_given#><dttm><int><chr><chr><chr><chr><chr>
-#>1 2026-06-26 10:11:05 1 MIC amoxicillin Escherich… human 8
-#>2 2026-06-26 10:11:06 1 MIC cipro Escherich… human 0.256
-#>3 2026-06-26 10:11:06 1 DISK tobra Escherich… human 16
-#>4 2026-06-26 10:11:06 1 DISK genta Escherich… human 18
+#>1 2026-06-26 13:09:56 1 MIC amoxicillin Escherich… human 8
+#>2 2026-06-26 13:09:56 1 MIC cipro Escherich… human 0.256
+#>3 2026-06-26 13:09:56 1 DISK tobra Escherich… human 16
+#>4 2026-06-26 13:09:56 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>
diff --git a/reference/as.sir.md b/reference/as.sir.md
index a11462f98..4f94072a7 100644
--- a/reference/as.sir.md
+++ b/reference/as.sir.md
@@ -712,10 +712,10 @@ sir_interpretation_history()
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#>
-#> 1 2026-06-26 10:11:05 1 MIC amoxicillin Escherich… human 8
-#> 2 2026-06-26 10:11:06 1 MIC cipro Escherich… human 0.256
-#> 3 2026-06-26 10:11:06 1 DISK tobra Escherich… human 16
-#> 4 2026-06-26 10:11:06 1 DISK genta Escherich… human 18
+#> 1 2026-06-26 13:09:56 1 MIC amoxicillin Escherich… human 8
+#> 2 2026-06-26 13:09:56 1 MIC cipro Escherich… human 0.256
+#> 3 2026-06-26 13:09:56 1 DISK tobra Escherich… human 16
+#> 4 2026-06-26 13:09:56 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab , mo , host , input ,
#> # outcome , notes , guideline , ref_table , uti ,
#> # breakpoint_S_R , site
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 9918cb21e..84b6dac8d 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 48f145171..c5ab961d2 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/av_property.html b/reference/av_property.html
index dc37e6785..9e400df53 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/availability.html b/reference/availability.html
index 5526be1eb..e504d30ce 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 1f90ba52e..6b1ce498d 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index f7f0185f4..76680a15b 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)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/count.html b/reference/count.html
index 31c30f397..3f0ec17eb 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)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/custom_interpretive_rules.html b/reference/custom_interpretive_rules.html
index 118242dc9..fd1388567 100644
--- a/reference/custom_interpretive_rules.html
+++ b/reference/custom_interpretive_rules.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html
index 63f4eb69c..dc47170bd 100644
--- a/reference/custom_mdro_guideline.html
+++ b/reference/custom_mdro_guideline.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/dosage.html b/reference/dosage.html
index f47ffec51..0a270cc30 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html
index c057bb0c9..58cdf229a 100644
--- a/reference/esbl_isolates.html
+++ b/reference/esbl_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index d30e0c042..3e3d2ff8d 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 6031a96b7..6b1ef3b9a 100644
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+ 3.0.1.9076
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
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index 082369b41..2a8e12a7f 100644
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diff --git a/reference/g.test.html b/reference/g.test.html
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diff --git a/reference/get_episode.html b/reference/get_episode.html
index b11cbf3ed..802ed688b 100644
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diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 12314458c..8f3f7592d 100644
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diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 4c54f9556..1a4ce803d 100644
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+ 3.0.1.9076
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
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diff --git a/reference/index.html b/reference/index.html
index f51ecee12..541220bcf 100644
--- a/reference/index.html
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diff --git a/reference/interpretive_rules.html b/reference/interpretive_rules.html
index 6b91a3e6c..1da713e16 100644
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@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive
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diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 4f7b91dcc..43586309d 100644
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diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index ffb370f64..3fccb13d2 100644
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diff --git a/reference/join.html b/reference/join.html
index 492413608..ce762848c 100644
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+ 3.0.1.9076
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index cee1ae665..981741c9e 100644
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diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 9d835ae44..0a788e299 100644
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diff --git a/reference/like.html b/reference/like.html
index 1267d75a3..7ae3d2fb0 100644
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diff --git a/reference/mdro.html b/reference/mdro.html
index 3dc01a0f1..3a7cc9424 100644
--- a/reference/mdro.html
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@@ -7,7 +7,7 @@
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+ 3.0.1.9076
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 269426b93..6030149ba 100644
--- a/reference/mean_amr_distance.html
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 5d7f744c1..9dae783eb 100644
--- a/reference/microorganisms.codes.html
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index b38ca6095..003074e0c 100644
--- a/reference/microorganisms.groups.html
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 9b98b73e8..1138347bd 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
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+ 3.0.1.9076
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 32674da14..7cf53533e 100644
--- a/reference/mo_matching_score.html
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@@ -7,7 +7,7 @@
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+ 3.0.1.9076
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 8d0799757..40f1e9ef9 100644
--- a/reference/mo_property.html
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 335f0cf06..e394bae5c 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)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/pca.html b/reference/pca.html
index 379065ba6..394351188 100644
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@@ -7,7 +7,7 @@
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+ 3.0.1.9076
diff --git a/reference/plot.html b/reference/plot.html
index c543dd274..cc3be7405 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)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/proportion.html b/reference/proportion.html
index 0d145baa2..bbe7fe1b3 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)
- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/random.html b/reference/random.html
index 594de4a37..ee3e55cf0 100644
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@@ -7,7 +7,7 @@
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 850acf783..3250345aa 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
AMR (for R)
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+ 3.0.1.9076
diff --git a/reference/skewness.html b/reference/skewness.html
index 22cb2b171..bf819693c 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
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index ca2d41fc7..ca4f9a944 100644
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+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/reference/translate.html b/reference/translate.html
index 7b2393e67..a22301723 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
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- 3.0.1.9075
+ 3.0.1.9076
diff --git a/search.json b/search.json
index 5b87ce530..28fca1c3a 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/CLAUDE.html","id":null,"dir":"","previous_headings":"","what":"CLAUDE.md — AMR R Package","title":"CLAUDE.md — AMR R Package","text":"file provides context Claude Code working repository.","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"project-overview","dir":"","previous_headings":"","what":"Project Overview","title":"CLAUDE.md — AMR R Package","text":"AMR zero-dependency R package antimicrobial resistance (AMR) data analysis using One Health approach. peer-reviewed, used 175+ countries, supports 28 languages. Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) disk diffusion handling - Multilingual output (28 languages)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"common-commands","dir":"","previous_headings":"","what":"Common Commands","title":"CLAUDE.md — AMR R Package","text":"commands run inside R session: shell:","code":"# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE) devtools::document() # Run all tests devtools::test() # Full package check (CRAN-level: docs + tests + checks) devtools::check() # Build pkgdown website locally pkgdown::build_site() # Code coverage report covr::package_coverage() # CRAN check from parent directory R CMD check AMR"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"repository-structure","dir":"","previous_headings":"","what":"Repository Structure","title":"CLAUDE.md — AMR R Package","text":"","code":"R/ # All R source files (62 files, ~28,000 lines) man/ # Auto-generated .Rd documentation (do not edit manually) tests/testthat/ # testthat test files (test-*.R) and helper-functions.R data/ # Pre-compiled .rda datasets data-raw/ # Scripts used to generate data/ files vignettes/ # Rmd vignette articles inst/ # Installed files (translations, etc.) _pkgdown.yml # pkgdown website configuration"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"r-source-file-conventions","dir":"","previous_headings":"","what":"R Source File Conventions","title":"CLAUDE.md — AMR R Package","text":"Naming conventions R/: Key source files: aa_helper_functions.R / aa_helper_pm_functions.R — internal utility functions (large; ~63 KB ~37 KB) aa_globals.R — global constants breakpoint lookup structures aa_options.R — amr_options() / get_AMR_option() system mo.R / mo_property.R — microorganism lookup properties ab.R / ab_property.R — antimicrobial drug functions av.R / av_property.R — antiviral drug functions sir.R / sir_calc.R / sir_df.R — SIR classification engine mic.R / disk.R — MIC disk diffusion classes antibiogram.R — antibiogram generation (traditional, combined, syndromic, WISCA) first_isolate.R — first-isolate identification algorithms mdro.R — MDRO classification (EUCAST, CLSI, CDC, custom guidelines) amr_selectors.R — tidyselect helpers selecting AMR columns interpretive_rules.R / custom_eucast_rules.R — clinical interpretation rules translate.R — 28-language translation system ggplot_sir.R / ggplot_pca.R / plotting.R — visualisation functions","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"custom-s3-classes","dir":"","previous_headings":"","what":"Custom S3 Classes","title":"CLAUDE.md — AMR R Package","text":"package defines five S3 classes full print/format/plot/vctrs support:","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"data-files","dir":"","previous_headings":"","what":"Data Files","title":"CLAUDE.md — AMR R Package","text":"Pre-compiled data/ (edit directly; regenerate via data-raw/ scripts):","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"zero-dependency-design","dir":"","previous_headings":"","what":"Zero-Dependency Design","title":"CLAUDE.md — AMR R Package","text":"package Imports DESCRIPTION. optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) listed Suggests guarded : Never add packages Imports. new functionality requires external package, add Suggests guard usage appropriately.","code":"if (requireNamespace(\"pkg\", quietly = TRUE)) { ... }"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"testing","dir":"","previous_headings":"","what":"Testing","title":"CLAUDE.md — AMR R Package","text":"Framework: testthat (R ≥ 3.1); legacy tinytest used R 3.0–3.6 CI Test files: tests/testthat/test-*.R Helpers: tests/testthat/helper-functions.R CI matrix: GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 oldrel-4 Coverage: covr (files excluded: atc_online.R, mo_source.R, translate.R, resistance_predict.R, zz_deprecated.R, helper files, zzz.R)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"CLAUDE.md — AMR R Package","text":"exported functions use roxygen2 blocks (RoxygenNote: 7.3.3, markdown enabled) Run devtools::document() change roxygen comments Never edit files man/ directly — auto-generated Vignettes live vignettes/ .Rmd files pkgdown website configured _pkgdown.yml","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"versioning","dir":"","previous_headings":"","what":"Versioning","title":"CLAUDE.md — AMR R Package","text":"Version format: major.minor.patch.dev (e.g., 3.0.1.9021) Development versions use .9xxx suffix Stable CRAN releases drop dev suffix (e.g., 3.0.1) NEWS.md uses sections New, Fixes, Updates GitHub issue references (#NNN)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"version-and-date-bump-required-for-every-pr","dir":"","previous_headings":"Versioning","what":"Version and date bump required for every PR","title":"CLAUDE.md — AMR R Package","text":"PRs squash-merged, PR lands exactly one commit default branch. Version numbers kept sync cumulative commit count since last released tag. Therefore exactly one version bump allowed per PR, regardless many intermediate commits made branch.","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"computing-the-correct-version-number","dir":"","previous_headings":"Versioning > Version and date bump required for every PR","what":"Computing the correct version number","title":"CLAUDE.md — AMR R Package","text":"First, ensure git gh installed — required version computation pushing changes. Install missing anything else: run following repo root determine version string use: + 1 accounts fact PR’s squash commit yet default branch. Set files resulting version string (per PR, even across multiple commits): DESCRIPTION — Version: field NEWS.md — replace line 1 (# AMR heading) new version number; create new section. NEWS.md continuous log entire current x.y.z.9nnn development series: changes since last stable release accumulate single heading. updating line 1, append new change bullet appropriate sub-heading (### New, ### Fixes, ### Updates). Style rules NEWS.md entries: extremely concise — one short line per item end full stop (period) verbose explanations; just essential fact git describe fails (e.g. tags exist environment), fall back reading current version DESCRIPTION adding 1 last numeric component — bump already made PR.","code":"which git || apt-get install -y git which gh || apt-get install -y gh # Also ensure all tags are fetched so git describe works git fetch --tags currenttag=$(git describe --tags --abbrev=0 | sed 's/v//') currenttagfull=$(git describe --tags --abbrev=0) defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$') git fetch origin ${defaultbranch} --quiet currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch}) currentversion=\"${currenttag}.$((currentcommit + 9001 + 1))\" echo \"$currentversion\""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"date-field","dir":"","previous_headings":"Versioning > Version and date bump required for every PR","what":"Date field","title":"CLAUDE.md — AMR R Package","text":"Date: field DESCRIPTION must reflect date last commit PR (first), ISO format. Update every commit always current:","code":"Date: 2026-03-07"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"internal-state","dir":"","previous_headings":"","what":"Internal State","title":"CLAUDE.md — AMR R Package","text":"package uses private AMR_env environment (created aa_globals.R) caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). avoids re-computation within session.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"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 drugs, 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":"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":"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":"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 07 May 2026. 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 #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class #> [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), Kosakonia pseudosacchari (0.471), Kaistella palustris #> (0.435), Kingella potus (0.435), and Kocuria palustris (0.435) #> ------------------------------------------------------------------------------- #> \"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), Streptomyces aureus (0.618), #> Staphylococcus auricularis (0.615), Streptomyces azureus (0.609), Salmonella #> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> and Staphylococcus argensis (0.587) #> ------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus parapneumoniae (0.714), Streptococcus #> pseudopneumoniae (0.700), Serratia proteamaculans quinivorans (0.557), #> Streptococcus phocae salmonis (0.552), Serratia proteamaculans quinovora #> (0.545), Sphingomonas piscinae (0.538), Streptococcus pseudoporcinus (0.536), #> Staphylococcus piscifermentans (0.533), Staphylococcus pseudintermedius #> (0.532), and Serratia proteamaculans proteamaculans (0.526) #> ℹ 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":"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":"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 Column first is SIR eligible (despite only having empty values), since it #> seems to be cefozopran (ZOP) #> 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":"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 bacteria #> Length :2724 Length :2724 Min. :2011-01-01 Class :mo #> N.unique : 260 N.unique : 3 1st Qu.:2013-04-07 :0 #> N.blank : 0 N.blank : 0 Median :2015-06-03 Unique:4 #> Min.nchar: 2 Min.nchar: 1 Mean :2015-06-09 #1 :B_ESCHR_COLI #> Max.nchar: 3 Max.nchar: 1 3rd Qu.:2017-08-11 #2 :B_STPHY_AURS #> Max. :2019-12-27 #3 :B_STRPT_PNMN #> AMX AMC CIP #> Class:sir Class:sir Class:sir #> %S :41.6% (n=1133) %S :52.6% (n=1432) %S :52.5% (n=1431) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I :16.4% (n=446) %I :12.2% (n=333) %I : 6.5% (n=176) #> %R :42.0% (n=1145) %R :35.2% (n=959) %R :41.0% (n=1117) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> GEN first #> Class:sir Mode:logical #> %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) #> %I : 3.0% (n=82) #> %R :36.0% (n=981) #> %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":"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":"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":"Conduct AMR data analysis","text":"AMR package supports 28 different languages antibiograms provides four types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373): Traditional Antibiogram (TA) – susceptibility species individual antibiotics Combination Antibiogram (CA) – susceptibility species combination regimens Syndromic Antibiogram (SA) – susceptibility species, stratified clinical syndrome setting Weighted-Incidence Syndromic Combination Antibiogram (WISCA) – estimated empirical coverage regimen syndrome, weighted pathogen incidence quantified uncertainty goal guide empirical therapy, WISCA default. reason simple: start empirical treatment, know pathogen causing infection. next patient present species label attached . matters probability regimen choose cover whatever pathogen turns cause, given local epidemiology syndrome. Traditional antibiograms answer question. fragment information species, ignore frequently species causes syndrome, evaluate combination regimens, provide measure uncertainty. WISCA addresses limitations using Bayesian framework (Hebert et al., 2012; Bielicki et al., 2016). See WISCA vignette full explanation. Traditional, combination, syndromic antibiograms remain useful surveillance purposes, .e., tracking resistance trends per species time. care clinical impact, choosing right empirical regimen patient, use WISCA. 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":"wisca-recommended-for-empirical-therapy-guidance","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"WISCA (recommended for empirical therapy guidance)","title":"Conduct AMR data analysis","text":"Use wisca() function, equivalently antibiogram(..., wisca = TRUE). WISCA produces single coverage estimate per regimen entire syndrome, weighted pathogen incidence, 95% credible interval Bayesian Monte Carlo simulation: output tells : “given species distribution data, estimated X% probability regimen covers infection, 95% credible interval [lower, upper]”. clinically relevant question. syndrome-specific patient-specific WISCA, use syndromic_group argument group data first. can stratify anything: ward, age group, risk profile, acquisition type. syndromic_group argument accepts column expression: Keep mind granular stratification produces relevant estimates subgroup, wider credible intervals due smaller sample sizes. always trade-granularity precision. local numbers small, consider pooling data multiple sites (Bielicki et al., 2016). reliable WISCA results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), since rare contaminants can distort coverage estimates. creating WISCA model, assessments can done distributions Monte Carlo simulations WISCA carried :","code":"wisca_result <- example_isolates %>% wisca( antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10 ) # Recommended threshold: ≥30 wisca_result wisca_out <- example_isolates %>% top_n_microorganisms(n = 10) %>% group_by( age_group = age_groups(age, c(25, 50, 75)), gender ) %>% wisca(antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) wisca_out wisca_plot(wisca_out) wisca_plot(wisca_out, wisca_plot_type = \"posterior_coverage\") # a ggplot2 extension for WISCAs and other antibiograms: ggplot2::autoplot(wisca_out)"},{"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":"Conduct AMR data analysis","text":"need per-species susceptibility rates, e.g., AMR surveillance reports, traditional antibiogram remains right tool. reports proportion susceptible isolates per species per antibiotic: 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, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. 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":"combination-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combination Antibiogram","title":"Conduct AMR data analysis","text":"combination antibiogram shows much additional susceptibility second agent adds given species. useful surveillance combination regimens, note still species-stratified account pathogen incidence syndrome:","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":"Conduct AMR data analysis","text":"syndromic antibiogram stratifies per-species susceptibility clinical context (ward, specimen type, etc.). adds clinical context traditional antibiogram still species-level, without incidence weighting uncertainty quantification. surveillance setting fine; empirical therapy guidance, WISCA preferred:","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":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"Conduct AMR data analysis","text":"antibiogram types, including WISCA, can plotted using autoplot() ggplot2 package, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(wisca_result)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"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) #> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> option to either \"CLSI\" or \"EUCAST\", see `?AMR-options`. #> ℹ This message will be shown once per session. #> [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":"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\") my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 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":"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 June 2026","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