aminoglycosides() can select: amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, kasugamycin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high
+aminoglycosides() can select: amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, kasugamycin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high
aminopenicillins() can select: amoxicillin, amoxicillin/clavulanic acid, and ampicillin
antifungals() can select: amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, and voriconazole
antimycobacterials() can select: 4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin
@@ -159,6 +159,7 @@
cephalosporins_5th() can select: ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, and ceftolozane/tazobactam
fluoroquinolones() can select: besifloxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levofloxacin/ornidazole, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, norfloxacin screening test, norfloxacin/metronidazole, norfloxacin/tinidazole, ofloxacin, ofloxacin/ornidazole, orbifloxacin, pazufloxacin, pefloxacin, pefloxacin screening test, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, and trovafloxacin
glycopeptides() can select: avoparcin, bleomycin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod, and zorbamycin
+ionophores() can select: lasalocid, monensin sodium, narasin, nystatin, and salinomycin
isoxazolylpenicillins() can select: cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, and oxacillin screening test
lincosamides() can select: clindamycin, clindamycin inducible screening test, lincomycin, and pirlimycin
lipoglycopeptides() can select: dalbavancin, oritavancin, and telavancin
diff --git a/reference/custom_mdro_guideline.md b/reference/custom_mdro_guideline.md
index 1b47d1e20..89e06219c 100644
--- a/reference/custom_mdro_guideline.md
+++ b/reference/custom_mdro_guideline.md
@@ -126,7 +126,7 @@ antimicrobial group:
all(glycopeptides() == "R") ~ "My MDRO #3"
)
-All 42 antimicrobial selectors are supported for use in the rules:
+All 43 antimicrobial selectors are supported for use in the rules:
- [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
can select:
@@ -312,6 +312,10 @@ All 42 antimicrobial selectors are supported for use in the rules:
ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin,
vancomycin, vancomycin-macromethod, and zorbamycin
+- [`ionophores()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+ can select:
+ lasalocid, monensin sodium, narasin, nystatin, and salinomycin
+
- [`isoxazolylpenicillins()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
can select:
cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin, and
diff --git a/reference/dosage.html b/reference/dosage.html
index ddd42ae03..13c35615e 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html
index 54140325b..cab35ffcc 100644
--- a/reference/esbl_isolates.html
+++ b/reference/esbl_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 3bd2d0dab..09643adb2 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index dde1a6490..6db9c43db 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index a4e227b49..e7445a87a 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 2a00f6194..457d29d21 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/g.test.html b/reference/g.test.html
index 4b546053e..54ada29bb 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/get_episode.html b/reference/get_episode.html
index 641e0548c..e7eec2075 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 341fd7042..b4da10439 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index a6d6ebcdd..95beaad5e 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 3f4e39a50..1a4aeada7 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/index.html b/reference/index.html
index b5194c160..20db3eec4 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
@@ -263,7 +263,7 @@
- aminoglycosides() aminopenicillins() antifungals() antimycobacterials() betalactams() betalactams_with_inhibitor() carbapenems() cephalosporins() cephalosporins_1st() cephalosporins_2nd() cephalosporins_3rd() cephalosporins_4th() cephalosporins_5th() fluoroquinolones() glycopeptides() isoxazolylpenicillins() lincosamides() lipoglycopeptides() macrolides() monobactams() nitrofurans() oxazolidinones() penicillins() peptides() phenicols() phosphonics() polymyxins() quinolones() rifamycins() spiropyrimidinetriones() streptogramins() sulfonamides() tetracyclines() trimethoprims() ureidopenicillins() amr_class() amr_selector() administrable_per_os() administrable_iv() not_intrinsic_resistant()
+ aminoglycosides() aminopenicillins() antifungals() antimycobacterials() betalactams() betalactams_with_inhibitor() carbapenems() cephalosporins() cephalosporins_1st() cephalosporins_2nd() cephalosporins_3rd() cephalosporins_4th() cephalosporins_5th() fluoroquinolones() glycopeptides() ionophores() isoxazolylpenicillins() lincosamides() lipoglycopeptides() macrolides() monobactams() nitrofurans() oxazolidinones() penicillins() peptides() phenicols() phosphonics() polymyxins() quinolones() rifamycins() spiropyrimidinetriones() streptogramins() sulfonamides() tetracyclines() trimethoprims() ureidopenicillins() amr_class() amr_selector() administrable_per_os() administrable_iv() not_intrinsic_resistant()
Antimicrobial Selectors
@@ -393,7 +393,7 @@
antimicrobials antibiotics antivirals
- Data Sets with 624 Antimicrobial Drugs
+ Data Sets with 625 Antimicrobial Drugs
diff --git a/reference/index.md b/reference/index.md
index 634b780ce..516cc3b38 100644
--- a/reference/index.md
+++ b/reference/index.md
@@ -252,6 +252,7 @@ or determine multi-drug resistant microorganisms (MDRO,
[`cephalosporins_5th()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
[`fluoroquinolones()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
[`glycopeptides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
+ [`ionophores()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
[`isoxazolylpenicillins()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
[`lincosamides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
[`lipoglycopeptides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
@@ -393,7 +394,7 @@ information about how to work with functions in this package.
- [`antimicrobials`](https://amr-for-r.org/reference/antimicrobials.md)
[`antibiotics`](https://amr-for-r.org/reference/antimicrobials.md)
[`antivirals`](https://amr-for-r.org/reference/antimicrobials.md) :
- Data Sets with 624 Antimicrobial Drugs
+ Data Sets with 625 Antimicrobial Drugs
- [`clinical_breakpoints`](https://amr-for-r.org/reference/clinical_breakpoints.md)
: Data Set with Clinical Breakpoints for SIR Interpretation
- [`example_isolates`](https://amr-for-r.org/reference/example_isolates.md)
diff --git a/reference/interpretive_rules.html b/reference/interpretive_rules.html
index cc8cb8962..f9adea8c5 100644
--- a/reference/interpretive_rules.html
+++ b/reference/interpretive_rules.html
@@ -13,7 +13,7 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index d40313bd9..da8ac0256 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/ionophores.html b/reference/ionophores.html
new file mode 100644
index 000000000..168688bb7
--- /dev/null
+++ b/reference/ionophores.html
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index afedb5062..f219545f9 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/join.html b/reference/join.html
index 3c71d198b..0f1f84be6 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 26dbb90e3..53db6444d 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index cf079734b..d793e91b6 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/like.html b/reference/like.html
index f1e0cc63a..b751cd81b 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/mdro.html b/reference/mdro.html
index 58e6a75ab..13f2a375a 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 7eddd7232..8d68e9da9 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 7dfe8b846..f6dc66ca2 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index dc09c7a8d..57d52ab8d 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 1ef13dc10..b9cf236f6 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 869e6a4db..3c7ecd777 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/mo_property.html b/reference/mo_property.html
index a007a17f0..8ac7ddc2c 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/mo_source.html b/reference/mo_source.html
index ca5db6bf0..0db478cd6 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/pca.html b/reference/pca.html
index f3fdc2091..08f08ea3f 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/plot.html b/reference/plot.html
index 3ea5e4af5..5b1fb0d49 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/proportion.html b/reference/proportion.html
index ec5f80b58..3cb3e54a8 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/random.html b/reference/random.html
index 2818b4ad7..33ed3488a 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 68fd3277f..940036b3d 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/skewness.html b/reference/skewness.html
index 32f7953b4..8916946a4 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)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index 270bffe60..50985d10b 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/reference/translate.html b/reference/translate.html
index f6f01eede..af26f029b 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9032
+ 3.0.1.9033
diff --git a/search.json b/search.json
index 02b5b50cd..61226f7cb 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":"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 — top-level heading # AMR git describe fails (e.g. tags exist environment), fall back reading current version DESCRIPTION adding 1 last numeric component — bump already made PR.","code":"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$') currentcommit=$(git rev-list --count ${currenttagfull}..${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 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":"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 #> 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":"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":"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":"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, 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":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"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":"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":"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":"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":"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 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 modelling 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 8.93 e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93 e- 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 2.22 e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , IPM , MEM # Evaluate model performance metrics <- predictions %>% metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics metrics #> # A tibble: 2 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 # To assess some other model properties, you can make our own `metrics()` function our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value metrics2 <- predictions %>% our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function metrics2 #> # A tibble: 4 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 #> 3 ppv binary 0.987 #> 4 npv binary 1 predictions %>% roc_curve(mo, `.pred_Gram-negative`) %>% autoplot()"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Conclusion","title":"AMR with tidymodels","text":"example, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antimicrobial classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-2-predicting-esbl-presence-using-raw-mics","dir":"Articles","previous_headings":"","what":"Example 2: Predicting ESBL Presence Using Raw MICs","title":"AMR with tidymodels","text":"second example, demonstrate use columns directly tidymodels workflows using AMR-specific recipe steps. includes transformation log2 scale using step_mic_log2(), prepares MIC values use classification models. approach idea formed basis publication DOI: 10.3389/fmicb.2025.1582703 model presence extended-spectrum beta-lactamases (ESBL) based MIC values.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective-1","dir":"Articles","previous_headings":"Example 2: Predicting ESBL Presence Using Raw MICs","what":"Objective","title":"AMR with tidymodels","text":"goal : Use raw MIC values predict whether bacterial isolate produces ESBL. Apply AMR-aware preprocessing tidymodels recipe. Train classification model evaluate predictive performance.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation-1","dir":"Articles","previous_headings":"Example 2: Predicting ESBL Presence Using Raw MICs","what":"Data Preparation","title":"AMR with tidymodels","text":"use esbl_isolates dataset comes AMR package. Explanation: esbl_isolates: Contains MIC test results ESBL status isolate. mutate(esbl = ...): Converts target column ordered factor classification.","code":"# Load required libraries library(AMR) library(tidymodels) # View the esbl_isolates data set esbl_isolates #> # A tibble: 500 × 19 #> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT #>