diff --git a/404.html b/404.html index 9897832c1..3a2b8e1e5 100644 --- a/404.html +++ b/404.html @@ -31,7 +31,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/CLAUDE.html b/CLAUDE.html index b4350e7cb..b3f352fc3 100644 --- a/CLAUDE.html +++ b/CLAUDE.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/LICENSE-text.html b/LICENSE-text.html index 5571d2e97..f946c676b 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/AMR.html b/articles/AMR.html index d9c593b65..5438805e3 100644 --- a/articles/AMR.html +++ b/articles/AMR.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html index f1948d766..4070fb981 100644 --- a/articles/AMR_for_Python.html +++ b/articles/AMR_for_Python.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html index c329e4f31..065ba5f0f 100644 --- a/articles/AMR_with_tidymodels.html +++ b/articles/AMR_with_tidymodels.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/EUCAST.html b/articles/EUCAST.html index e133da0ee..c6411b49e 100644 --- a/articles/EUCAST.html +++ b/articles/EUCAST.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/PCA.html b/articles/PCA.html index aee73cda9..45ff3e304 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/WHONET.html b/articles/WHONET.html index 8c3b53456..2539880fd 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/WISCA.html b/articles/WISCA.html index cda79fa83..cc3ab9ffd 100644 --- a/articles/WISCA.html +++ b/articles/WISCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/datasets.html b/articles/datasets.html index dfefca2ef..a445e46b5 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/articles/index.html b/articles/index.html index 8ec5e0b8c..bce1f79d8 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/authors.html b/authors.html index 8ce12a337..cade3efd1 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/index.html b/index.html index 64b67bc22..4086e19a6 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/news/index.html b/news/index.html index da15c8e34..eb40f9030 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 @@ -49,10 +49,10 @@ -AMR 3.0.1.9088 +AMR 3.0.1.9089 Planned as v3.1.0, end of September 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. @@ -105,7 +105,7 @@ 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) @@ -122,7 +122,7 @@ New wisca_plot() to assess the susceptibility and incidence distributions from the Monte Carlo simulations -Fixed +Fixed Setting options(AMR_guideline = "EUCAST 2012") or any year-qualified value no longer causes errors or silent wrong behaviour in interpretive_rules(), resistance(), susceptibility(), count_resistant(), count_susceptible(), and SIR plotting/printing functions (#298) as.sir() @@ -151,7 +151,7 @@ -Updated +Updated top_n_microorganisms(): new property_for_each argument for sub-grouping within top n groups; rank ordering enforced (only lower taxonomic ranks allowed); fixed property = NULL not being accepted; inner filter now tracks original row indices to prevent cross-group contamination Taxonomic update for all microorganisms, now updated to June 2026 diff --git a/news/index.md b/news/index.md index 03b79eabb..2b01f0428 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9088 +## AMR 3.0.1.9089 Planned as v3.1.0, end of September 2026. diff --git a/pkgdown.yml b/pkgdown.yml index a3b6d26e0..249ea8e69 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-09-03T12:29Z +last_built: 2026-09-03T14:05Z 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 848a12735..6638979b2 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index f1e10be81..4e0a569bd 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -9,7 +9,7 @@ options(AMR_guideline = "CLSI")'>AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/AMR.html b/reference/AMR.html index 82888f9f7..5cd0013b1 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 81b86dead..3f301a9a5 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/WHONET.html b/reference/WHONET.html index 876aacba6..e1aeed3b8 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 38ff95b33..e99c3068e 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/ab_property.html b/reference/ab_property.html index 15c41d76b..81ae57686 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 397b3d4bd..5e4468715 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index e30244ae9..a1790e146 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/age.html b/reference/age.html index c79041fd0..5042160b5 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/age_groups.html b/reference/age_groups.html index c04f0bcdc..11ad51601 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index 239a073c8..d72807882 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/amr_course.html b/reference/amr_course.html index 21b76f9f1..eee8f6a71 100644 --- a/reference/amr_course.html +++ b/reference/amr_course.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index babc54faf..b1eb2aa21 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html index d1aeb61bf..40556ae33 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index 22b3aa1a1..1fc197013 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.ab.html b/reference/as.ab.html index e844cf157..2fa2151a0 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.av.html b/reference/as.av.html index b1bc22877..eb60933e1 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.disk.html b/reference/as.disk.html index 1a6247d69..047cff962 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.mic.html b/reference/as.mic.html index ea1848a61..067fa95db 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.mo.html b/reference/as.mo.html index e84c50c24..7418bd20d 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/as.sir.html b/reference/as.sir.html index 1634f874e..801a023a4 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) - 3.0.1.9088 + 3.0.1.9089 @@ -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 2026-09-03 12:31:42 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-09-03 12:31:42 1 MIC cipro Escherich… human 0.256 -#> 3 2026-09-03 12:31:42 1 DISK tobra Escherich… human 16 -#> 4 2026-09-03 12:31:43 1 DISK genta Escherich… human 18 +#> 1 2026-09-03 14:07:40 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-09-03 14:07:40 1 MIC cipro Escherich… human 0.256 +#> 3 2026-09-03 14:07:40 1 DISK tobra Escherich… human 16 +#> 4 2026-09-03 14:07:41 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 ed1a2bec2..ddc2e1979 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 2026-09-03 12:31:42 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-09-03 12:31:42 1 MIC cipro Escherich… human 0.256 -#> 3 2026-09-03 12:31:42 1 DISK tobra Escherich… human 16 -#> 4 2026-09-03 12:31:43 1 DISK genta Escherich… human 18 +#> 1 2026-09-03 14:07:40 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-09-03 14:07:40 1 MIC cipro Escherich… human 0.256 +#> 3 2026-09-03 14:07:40 1 DISK tobra Escherich… human 16 +#> 4 2026-09-03 14:07:41 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 c1a3c5429..7aae84bbf 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 3f6343dbb..575525735 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/av_property.html b/reference/av_property.html index e45a82b1d..ed416b5c0 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/availability.html b/reference/availability.html index 14dbd675d..2f3b19bd2 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index 8201f125f..8cc241290 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 631ebac6d..63c7b8a23 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/count.html b/reference/count.html index 9ff042d22..321492f71 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/custom_interpretive_rules.html b/reference/custom_interpretive_rules.html index 39623b667..4986688a0 100644 --- a/reference/custom_interpretive_rules.html +++ b/reference/custom_interpretive_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index 630b69926..3e52bdb06 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/dosage.html b/reference/dosage.html index 2044b047e..9c30b5c64 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index a3d7ff483..a1ccaa565 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 377b50cc8..8ae2fdfa9 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index cf4dc2dd7..f08ee0978 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index 8bef66609..8a4eade42 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 3567ce6cf..d7adb7f19 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/g.test.html b/reference/g.test.html index e0e7d1f74..09bb30ce7 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/get_episode.html b/reference/get_episode.html index 2b3ef1588..f17ce844c 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 93d32b00f..475f60f4a 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 9b9669238..c6edbeb80 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index e78d3be69..bef48e30d 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/index.html b/reference/index.html index 1f090d1c2..686c8206c 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/interpretive_rules.html b/reference/interpretive_rules.html index 4d7adf65e..0bc204a3b 100644 --- a/reference/interpretive_rules.html +++ b/reference/interpretive_rules.html @@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive AMR (for R) - 3.0.1.9088 + 3.0.1.9089 @@ -135,11 +135,11 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive version_expertrules -The version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1". +The version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3". ampc_cephalosporin_resistance -(only applies when rules contains "expert" or "all") a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is NA. Currently only works when version_expertrules is 3.2 and higher; these versions of 'EUCAST Expert Rules on Enterobacterales' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of NA (the default) for this argument will remove results for these three drugs, while e.g. a value of "R" will make the results for these drugs resistant. Use NULL or FALSE to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using TRUE is equal to using "R". For EUCAST Expert Rules v3.2, this rule applies to: Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, and Serratia. +(only applies when rules contains "expert" or "all") a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is NA. Currently only works when version_expertrules is 3.2 and higher; these versions of 'EUCAST Expert Rules on Enterobacterales' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of NA (the default) for this argument will remove results for these three drugs, while e.g. a value of "R" will make the results for these drugs resistant. Use NULL or FALSE to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using TRUE is equal to using "R". For EUCAST Expert Rules v3.2, this rule applies to: Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, and Klebsiella aerogenes. only_sir_columns diff --git a/reference/interpretive_rules.md b/reference/interpretive_rules.md index 64a07d176..758d3502d 100644 --- a/reference/interpretive_rules.md +++ b/reference/interpretive_rules.md @@ -118,7 +118,7 @@ eucast_dosage(ab, administration = "iv", version_breakpoints = 15) - version_expertrules: The version number to use for the EUCAST Expert Rules and Intrinsic - Resistance guideline. Can be `"3.3"`, `"3.2"`, or `"3.1"`. + Resistance guideline. Can be `"3.3"`. - ampc_cephalosporin_resistance: @@ -140,8 +140,7 @@ eucast_dosage(ab, administration = "iv", version_breakpoints = 15) braakii*, *Citrobacter freundii*, *Citrobacter gillenii*, *Citrobacter murliniae*, *Citrobacter rodenticum*, *Citrobacter sedlakii*, *Citrobacter werkmanii*, *Citrobacter youngae*, *Enterobacter*, - *Hafnia alvei*, *Klebsiella aerogenes*, *Morganella morganii*, - *Providencia*, and *Serratia*. + *Hafnia alvei*, and *Klebsiella aerogenes*. - only_sir_columns: diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index f8597c7a7..fe199d930 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index 8a68f2165..a42e53b73 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/join.html b/reference/join.html index 0149822e4..cefd4d197 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 97ada234c..4ddc90e82 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index 1679e9501..62647ecda 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/like.html b/reference/like.html index a6e85fe73..b6e73a68e 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/mdro.html b/reference/mdro.html index 1261416ab..62f35bf92 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index b469cd49e..ed272e455 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 840f2ce7e..d6b606403 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index 1cb0893a0..f9cc91397 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index 7f3c01e88..48256fc9f 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 0adfc3dac..4360972b9 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/mo_property.html b/reference/mo_property.html index a24424620..210076d58 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/mo_source.html b/reference/mo_source.html index 2232eff22..34cb85715 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/pca.html b/reference/pca.html index 821b3be00..d98226ca2 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/plot.html b/reference/plot.html index e3feb2c23..2ce3cc5a2 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/proportion.html b/reference/proportion.html index d918b3d42..2ec4270b2 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/random.html b/reference/random.html index e2bd12aa9..841ace5bb 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 07d522456..5806c01b4 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/skewness.html b/reference/skewness.html index 71f589058..d911272e7 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) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index 58c8e8e69..fe42faf00 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/reference/translate.html b/reference/translate.html index b13ace98f..6694fe044 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9088 + 3.0.1.9089 diff --git a/search.json b/search.json index e82f0ac30..8ffed2360 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":"code-style","dir":"","previous_headings":"","what":"Code Style","title":"CLAUDE.md — AMR R Package","text":"Follow tidyverse style guide precisely. Key rules: 2-space indentation; tabs <- assignment, = Spaces around binary operators commas; spaces inside parentheses function call must break across lines, place first argument new line indented 2 spaces, put closing ) line — never align arguments opening parenthesis (hanging/forced mid-line indentation)","code":"# good stop_( \"some long message part one \", \"part two\" ) # bad — forces indentation to match the opening parenthesis stop_(\"some long message part one \", \"part two\")"},{"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) #> ------------------------------------------------------------------------------- #> \"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-antimicrobial-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antimicrobial selectors","title":"Conduct AMR data analysis","text":"Using -called antimicrobial class selectors, can select filter columns based antimicrobial class antimicrobial 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 antimicrobial 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) #> [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":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"stable-release-cran","dir":"Articles","previous_headings":"Installation Channels","what":"Stable Release (CRAN)","title":"AMR for Python","text":"default AMR Python package uses latest stable version AMR R package, published CRAN. running pip install AMR, import usual:","code":"import AMR AMR.example_isolates"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"development-version-github","dir":"Articles","previous_headings":"Installation Channels","what":"Development Version (GitHub)","title":"AMR for Python","text":"use latest development version AMR R package (sourced directly GitHub), import beta sub-package alias AMR: Aliasing AMR keeps downstream code identical stable import. Switching stable release development version requires changing import line — nothing else script needs change.","code":"import AMR.beta as AMR AMR.example_isolates"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"using-enforce_method","dir":"Articles","previous_headings":"SIR Classification with as_sir()","what":"Using enforce_method","title":"AMR for Python","text":"as_sir() function R uses S3 method dispatch select correct calculation method based input class: MIC values disk diffusion values. Python objects carry R class attributes rpy2 bridge, automatic dispatch may resolve correctly. explicitly specify input type, use enforce_method argument: Without enforce_method, R falls back class-based dispatch raw Python input, may fail return unexpected results. Always supply enforce_method calling as_sir() Python.","code":"# 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\")"},{"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
Planned as v3.1.0, end of September 2026.
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.
domain
microorganisms
mo_kingdom()
"Pseudomonadati"
"Bacteria"
mo_domain()
kingdom
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.
future
as.sir()
wisca()
future::plan(future::multisession)
parallel = TRUE
clinical_breakpoints
breakpoint_type = "ECOFF"
wisca_plot()
options(AMR_guideline = "EUCAST 2012")
interpretive_rules()
resistance()
susceptibility()
count_resistant()
count_susceptible()
top_n_microorganisms()
property_for_each
property = NULL
The version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1".
"3.3"
"3.2"
"3.1"
The version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3".
(only applies when rules contains "expert" or "all") a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is NA. Currently only works when version_expertrules is 3.2 and higher; these versions of 'EUCAST Expert Rules on Enterobacterales' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of NA (the default) for this argument will remove results for these three drugs, while e.g. a value of "R" will make the results for these drugs resistant. Use NULL or FALSE to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using TRUE is equal to using "R". For EUCAST Expert Rules v3.2, this rule applies to: Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, and Serratia.
rules
"expert"
"all"
NA
version_expertrules
3.2
"R"
NULL
FALSE
TRUE
(only applies when rules contains "expert" or "all") a character value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is NA. Currently only works when version_expertrules is 3.2 and higher; these versions of 'EUCAST Expert Rules on Enterobacterales' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of NA (the default) for this argument will remove results for these three drugs, while e.g. a value of "R" will make the results for these drugs resistant. Use NULL or FALSE to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using TRUE is equal to using "R". For EUCAST Expert Rules v3.2, this rule applies to: Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, and Klebsiella aerogenes.