#> Groups (n=4, named as 'order'): diff --git a/articles/PCA.md b/articles/PCA.md index 79cec35ca..879ba066a 100644 --- a/articles/PCA.md +++ b/articles/PCA.md @@ -123,7 +123,7 @@ summary(pca_result) #> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales" #> Importance of components: #> PC1 PC2 PC3 PC4 PC5 PC6 PC7 -#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17 +#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 ``` diff --git a/articles/WHONET.html b/articles/WHONET.html index 905927360..5feef583c 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/articles/WISCA.html b/articles/WISCA.html index 004e21b41..706dbfd05 100644 --- a/articles/WISCA.html +++ b/articles/WISCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/articles/datasets.html b/articles/datasets.html index 706f303ea..2e5c7e1b9 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/articles/index.html b/articles/index.html index a1bd98701..e6f3d5839 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/authors.html b/authors.html index 4059b978d..5e6e3215f 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/index.html b/index.html index 2fe1132ce..fef853b0e 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/news/index.html b/news/index.html index 8a9ab17bc..638f5c41e 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 @@ -49,15 +49,15 @@ -AMR 3.0.1.9073 +AMR 3.0.1.9074 Planned as v3.1.0, end of June 2026. -Breaking Changes +Breaking Changes The former kingdoms Bacteria and Archaea are now each divided into four kingdoms with new top-level domains ‘Bacteria’ and ‘Archaea’ (Göker and Oren, 2024, DOI: 10.1099/ijsem.0.006242). Following this, a new domain column in the microorganisms data set was added, and more importantly, mo_kingdom() now returns the formal kingdom (e.g. "Pseudomonadati" instead of "Bacteria"). Use mo_domain() for the old behaviour. For non-prokaryotic kingdoms (Fungi, Protozoa, etc.), kingdom and domain are identical. Faster parallel computing via the future package for as.sir() and wisca(): a non-sequential plan (e.g. future::plan(future::multisession)) must be active before using parallel = TRUE. -New +New EUCAST 2026 and CLSI 2026 breakpoints: over 5,700 new breakpoints added to the clinical_breakpoints data set; EUCAST 2026 is now the default for all MIC and disk diffusion interpretations Wildtype/Non-wildtype (WT/NWT) output when using ECOFF-based interpretation, by setting breakpoint_type = "ECOFF" in as.sir(); WT/NWT results are fully supported in all resistance/susceptibility functions and plots (#254) @@ -74,7 +74,7 @@ New wisca_plot() to assess the susceptibility and incidence distributions from the Monte Carlo simulations -Fixed +Fixed as.sir() On data frames: already-converted SIR columns no longer dropped on re-run (#278) @@ -101,7 +101,7 @@ -Updated +Updated Taxonomic update for all microorganisms, now updated to June 2026 mo_kingdom() now returns the formal taxonomic kingdom; a one-time note per session explains the change when querying bacterial or archaeal records. diff --git a/news/index.md b/news/index.md index ca001a24c..61b16ceb2 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9073 +## AMR 3.0.1.9074 Planned as v3.1.0, end of June 2026. diff --git a/pkgdown.yml b/pkgdown.yml index 042de1256..dcc84e7b5 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-06-26T09:54Z +last_built: 2026-06-26T10:01Z 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 b8a430de3..77abccafc 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index b1ce25a25..638d73642 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -9,7 +9,7 @@ options(AMR_guideline = "CLSI")'>AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/AMR.html b/reference/AMR.html index 4bd376f41..b8ecef7e0 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 814b162a3..5521e7af7 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/WHONET.html b/reference/WHONET.html index 9ed3f7844..5021c0fb9 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index ff7cc0603..001970be7 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/ab_property.html b/reference/ab_property.html index 2ab0c017b..c438f5d7c 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 37e232e87..24032e9e4 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index b614ddbe6..8b2eae7c9 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/age.html b/reference/age.html index 2ba7d1fb2..e64e4de0e 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/age_groups.html b/reference/age_groups.html index 1d46a6b57..9d5993116 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index 57b8aa12a..25ec31673 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/amr_course.html b/reference/amr_course.html index 8716c76b8..b691bba61 100644 --- a/reference/amr_course.html +++ b/reference/amr_course.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index 633888abd..86900729e 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html index 773ce80ed..8c3190ebc 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index cc0714d1e..ea15c1c6f 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.ab.html b/reference/as.ab.html index 244f8013f..b5ebb1542 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.av.html b/reference/as.av.html index 582d801ae..544aa03d3 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.disk.html b/reference/as.disk.html index 54223116d..50b441b67 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.mic.html b/reference/as.mic.html index 427532e49..75a689475 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.mo.html b/reference/as.mo.html index 1d482e2ac..01e4e6b6c 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/as.sir.html b/reference/as.sir.html index 1b1c74090..21815f864 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) - 3.0.1.9073 + 3.0.1.9074 @@ -462,10 +462,10 @@ Breakpoints are currently implemented from EUCAST 2011-2026 and CLSI 2011-2026, #> # A tibble: 4 × 18 #> datetime index method ab_given mo_given host_given input_given #> <dttm> <int> <chr> <chr> <chr> <chr> <chr> -#> 1 2026-06-26 09:56:29 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-06-26 09:56:29 1 MIC cipro Escherich… human 0.256 -#> 3 2026-06-26 09:56:29 1 DISK tobra Escherich… human 16 -#> 4 2026-06-26 09:56:29 1 DISK genta Escherich… human 18 +#> 1 2026-06-26 10:03:26 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-06-26 10:03:27 1 MIC cipro Escherich… human 0.256 +#> 3 2026-06-26 10:03:27 1 DISK tobra Escherich… human 16 +#> 4 2026-06-26 10:03:27 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 559ab10d0..ff1ed3c30 100644 --- a/reference/as.sir.md +++ b/reference/as.sir.md @@ -712,10 +712,10 @@ sir_interpretation_history() #> # A tibble: 4 × 18 #> datetime index method ab_given mo_given host_given input_given #> -#> 1 2026-06-26 09:56:29 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-06-26 09:56:29 1 MIC cipro Escherich… human 0.256 -#> 3 2026-06-26 09:56:29 1 DISK tobra Escherich… human 16 -#> 4 2026-06-26 09:56:29 1 DISK genta Escherich… human 18 +#> 1 2026-06-26 10:03:26 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-06-26 10:03:27 1 MIC cipro Escherich… human 0.256 +#> 3 2026-06-26 10:03:27 1 DISK tobra Escherich… human 16 +#> 4 2026-06-26 10:03:27 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 68d561a12..3bea5bc88 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index dd449a0b5..cafffdf09 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/av_property.html b/reference/av_property.html index fefda9f88..fe414d5b8 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/availability.html b/reference/availability.html index dea93e185..bcc241a4f 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index 7ec8fd05a..ad8bbeaeb 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index f5378e247..8ea96c3fc 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/count.html b/reference/count.html index 7812e10a1..41dbad769 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/custom_interpretive_rules.html b/reference/custom_interpretive_rules.html index 50a9fe4e7..f5b9c1174 100644 --- a/reference/custom_interpretive_rules.html +++ b/reference/custom_interpretive_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index b5acd5b05..52695888c 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/dosage.html b/reference/dosage.html index 4c2de0773..6b9e82ef3 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index 71f9589ec..f28321a9f 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 1fd4c3ff5..481029e88 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index 5a3054ef1..29da09b97 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index fe415605f..62a0cf912 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 84dd28b8a..da752834d 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/g.test.html b/reference/g.test.html index 69fd20b50..daa211c88 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/get_episode.html b/reference/get_episode.html index 25bb58d97..23281a095 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 468db8ebd..d62b6de8f 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 3b579a049..8ec9b88a2 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 168801ebc..ceeb62f61 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/index.html b/reference/index.html index 7ec4131e0..523f7713e 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/interpretive_rules.html b/reference/interpretive_rules.html index 0d6318f53..8417aff96 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index f083914aa..147bdfba1 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index abacf8464..8c6b79402 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/join.html b/reference/join.html index b1bd9b19e..ee0f468ad 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 9476dbe8c..7f0a99fc8 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index b2fefff1e..32cea9a6a 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/like.html b/reference/like.html index 1b4e87bc8..258585f7a 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/mdro.html b/reference/mdro.html index 5bee54230..8d82f7692 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 8a954a257..6100f147c 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 3ac927a37..cbfbc1059 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index 0c0b210d8..5117ce0f4 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index 0cc49b533..f248ba052 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 21f173d7e..c3e2ba5e4 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/mo_property.html b/reference/mo_property.html index 8316dc25b..859cee6eb 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/mo_source.html b/reference/mo_source.html index 5917647fe..d69b38229 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/pca.html b/reference/pca.html index b71d0795d..20b5d338c 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/plot.html b/reference/plot.html index a28ea97a9..85c77d5bf 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/proportion.html b/reference/proportion.html index ff6a4c6c1..ec9348205 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/random.html b/reference/random.html index 22c0bac50..ffc24d495 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 4fb35f5ad..55004a04a 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/skewness.html b/reference/skewness.html index 77a23f878..b93d689ba 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) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index 353e75e5f..b020b6974 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/reference/translate.html b/reference/translate.html index 8988533d1..9825a2c21 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9073 + 3.0.1.9074 diff --git a/search.json b/search.json index e359b9939..42325804f 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://amr-for-r.org/CLAUDE.html","id":null,"dir":"","previous_headings":"","what":"CLAUDE.md — AMR R Package","title":"CLAUDE.md — AMR R Package","text":"file provides context Claude Code working repository.","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"project-overview","dir":"","previous_headings":"","what":"Project Overview","title":"CLAUDE.md — AMR R Package","text":"AMR zero-dependency R package antimicrobial resistance (AMR) data analysis using One Health approach. peer-reviewed, used 175+ countries, supports 28 languages. Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) disk diffusion handling - Multilingual output (28 languages)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"common-commands","dir":"","previous_headings":"","what":"Common Commands","title":"CLAUDE.md — AMR R Package","text":"commands run inside R session: shell:","code":"# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE) devtools::document() # Run all tests devtools::test() # Full package check (CRAN-level: docs + tests + checks) devtools::check() # Build pkgdown website locally pkgdown::build_site() # Code coverage report covr::package_coverage() # CRAN check from parent directory R CMD check AMR"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"repository-structure","dir":"","previous_headings":"","what":"Repository Structure","title":"CLAUDE.md — AMR R Package","text":"","code":"R/ # All R source files (62 files, ~28,000 lines) man/ # Auto-generated .Rd documentation (do not edit manually) tests/testthat/ # testthat test files (test-*.R) and helper-functions.R data/ # Pre-compiled .rda datasets data-raw/ # Scripts used to generate data/ files vignettes/ # Rmd vignette articles inst/ # Installed files (translations, etc.) _pkgdown.yml # pkgdown website configuration"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"r-source-file-conventions","dir":"","previous_headings":"","what":"R Source File Conventions","title":"CLAUDE.md — AMR R Package","text":"Naming conventions R/: Key source files: aa_helper_functions.R / aa_helper_pm_functions.R — internal utility functions (large; ~63 KB ~37 KB) aa_globals.R — global constants breakpoint lookup structures aa_options.R — amr_options() / get_AMR_option() system mo.R / mo_property.R — microorganism lookup properties ab.R / ab_property.R — antimicrobial drug functions av.R / av_property.R — antiviral drug functions sir.R / sir_calc.R / sir_df.R — SIR classification engine mic.R / disk.R — MIC disk diffusion classes antibiogram.R — antibiogram generation (traditional, combined, syndromic, WISCA) first_isolate.R — first-isolate identification algorithms mdro.R — MDRO classification (EUCAST, CLSI, CDC, custom guidelines) amr_selectors.R — tidyselect helpers selecting AMR columns interpretive_rules.R / custom_eucast_rules.R — clinical interpretation rules translate.R — 28-language translation system ggplot_sir.R / ggplot_pca.R / plotting.R — visualisation functions","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"custom-s3-classes","dir":"","previous_headings":"","what":"Custom S3 Classes","title":"CLAUDE.md — AMR R Package","text":"package defines five S3 classes full print/format/plot/vctrs support:","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"data-files","dir":"","previous_headings":"","what":"Data Files","title":"CLAUDE.md — AMR R Package","text":"Pre-compiled data/ (edit directly; regenerate via data-raw/ scripts):","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"zero-dependency-design","dir":"","previous_headings":"","what":"Zero-Dependency Design","title":"CLAUDE.md — AMR R Package","text":"package Imports DESCRIPTION. optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) listed Suggests guarded : Never add packages Imports. new functionality requires external package, add Suggests guard usage appropriately.","code":"if (requireNamespace(\"pkg\", quietly = TRUE)) { ... }"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"testing","dir":"","previous_headings":"","what":"Testing","title":"CLAUDE.md — AMR R Package","text":"Framework: testthat (R ≥ 3.1); legacy tinytest used R 3.0–3.6 CI Test files: tests/testthat/test-*.R Helpers: tests/testthat/helper-functions.R CI matrix: GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 oldrel-4 Coverage: covr (files excluded: atc_online.R, mo_source.R, translate.R, resistance_predict.R, zz_deprecated.R, helper files, zzz.R)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"CLAUDE.md — AMR R Package","text":"exported functions use roxygen2 blocks (RoxygenNote: 7.3.3, markdown enabled) Run devtools::document() change roxygen comments Never edit files man/ directly — auto-generated Vignettes live vignettes/ .Rmd files pkgdown website configured _pkgdown.yml","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"versioning","dir":"","previous_headings":"","what":"Versioning","title":"CLAUDE.md — AMR R Package","text":"Version format: major.minor.patch.dev (e.g., 3.0.1.9021) Development versions use .9xxx suffix Stable CRAN releases drop dev suffix (e.g., 3.0.1) NEWS.md uses sections New, Fixes, Updates GitHub issue references (#NNN)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"version-and-date-bump-required-for-every-pr","dir":"","previous_headings":"Versioning","what":"Version and date bump required for every PR","title":"CLAUDE.md — AMR R Package","text":"PRs squash-merged, PR lands exactly one commit default branch. Version numbers kept sync cumulative commit count since last released tag. Therefore exactly one version bump allowed per PR, regardless many intermediate commits made branch.","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"computing-the-correct-version-number","dir":"","previous_headings":"Versioning > Version and date bump required for every PR","what":"Computing the correct version number","title":"CLAUDE.md — AMR R Package","text":"First, ensure git gh installed — required version computation pushing changes. Install missing anything else: run following repo root determine version string use: + 1 accounts fact PR’s squash commit yet default branch. Set files resulting version string (per PR, even across multiple commits): DESCRIPTION — Version: field NEWS.md — replace line 1 (# AMR heading) new version number; create new section. NEWS.md continuous log entire current x.y.z.9nnn development series: changes since last stable release accumulate single heading. updating line 1, append new change bullet appropriate sub-heading (### New, ### Fixes, ### Updates). Style rules NEWS.md entries: extremely concise — one short line per item end full stop (period) verbose explanations; just essential fact git describe fails (e.g. tags exist environment), fall back reading current version DESCRIPTION adding 1 last numeric component — bump already made PR.","code":"which git || apt-get install -y git which gh || apt-get install -y gh # Also ensure all tags are fetched so git describe works git fetch --tags currenttag=$(git describe --tags --abbrev=0 | sed 's/v//') currenttagfull=$(git describe --tags --abbrev=0) defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$') git fetch origin ${defaultbranch} --quiet currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch}) currentversion=\"${currenttag}.$((currentcommit + 9001 + 1))\" echo \"$currentversion\""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"date-field","dir":"","previous_headings":"Versioning > Version and date bump required for every PR","what":"Date field","title":"CLAUDE.md — AMR R Package","text":"Date: field DESCRIPTION must reflect date last commit PR (first), ISO format. Update every commit always current:","code":"Date: 2026-03-07"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"internal-state","dir":"","previous_headings":"","what":"Internal State","title":"CLAUDE.md — AMR R Package","text":"package uses private AMR_env environment (created aa_globals.R) caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). avoids re-computation within session.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial drugs, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"Conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"Conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"Conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 07 May 2026. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the `microorganisms.codes` data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> `mo_uncertainties()` to review these uncertainties, or use #> `add_custom_microorganisms()` to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> ------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli (0.643), #> Escherichia coli expressing (0.611), Enterobacter cowanii (0.600), Enterococcus #> columbae (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus #> (0.577), Enterobacter cloacae cloacae (0.571), Enterobacter cloacae complex #> (0.571), and Enterobacter cloacae dissolvens (0.565) #> ------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella pneumoniae #> ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), Klebsiella #> pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii (0.500), Klebsiella #> planticola (0.500), Kosakonia pseudosacchari (0.471), Kaistella palustris #> (0.435), Kingella potus (0.435), and Kocuria palustris (0.435) #> ------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus argenteus #> (0.625), Staphylococcus aureus anaerobius (0.625), Streptomyces aureus (0.618), #> Staphylococcus auricularis (0.615), Streptomyces azureus (0.609), Salmonella #> Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> and Staphylococcus argensis (0.587) #> ------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus parapneumoniae (0.714), Streptococcus #> pseudopneumoniae (0.700), Serratia proteamaculans quinivorans (0.557), #> Streptococcus phocae salmonis (0.552), Serratia proteamaculans quinovora #> (0.545), Sphingomonas piscinae (0.538), Streptococcus pseudoporcinus (0.536), #> Staphylococcus piscifermentans (0.533), Staphylococcus pseudintermedius #> (0.532), and Serratia proteamaculans proteamaculans (0.526) #> ℹ Only the first 10 other matches of each record are shown. Run `` #> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save #> `mo_uncertainties()` to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"Conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"Conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column bacteria as input for `col_mo`. #> ℹ Column first is SIR eligible (despite only having empty values), since it #> seems to be cefozopran (ZOP) #> ℹ Using column date as input for `col_date`. #> ℹ Using column patient_id as input for `col_patient_id`. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a #> microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,724 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,714 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"Conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date bacteria #> Length :2724 Length :2724 Min. :2011-01-01 Class :mo #> N.unique : 260 N.unique : 3 1st Qu.:2013-04-07 :0 #> N.blank : 0 N.blank : 0 Median :2015-06-03 Unique:4 #> Min.nchar: 2 Min.nchar: 1 Mean :2015-06-09 #1 :B_ESCHR_COLI #> Max.nchar: 3 Max.nchar: 1 3rd Qu.:2017-08-11 #2 :B_STPHY_AURS #> Max. :2019-12-27 #3 :B_STRPT_PNMN #> AMX AMC CIP #> Class:sir Class:sir Class:sir #> %S :41.6% (n=1133) %S :52.6% (n=1432) %S :52.5% (n=1431) #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I :16.4% (n=446) %I :12.2% (n=333) %I : 6.5% (n=176) #> %R :42.0% (n=1145) %R :35.2% (n=959) %R :41.0% (n=1117) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> GEN first #> Class:sir Mode:logical #> %S :61.0% (n=1661) TRUE:2724 #> %SDD : 0.0% (n=0) #> %I : 3.0% (n=82) #> %R :36.0% (n=981) #> %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1854 4 3 3 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"Conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1321 #> 2 Staphylococcus aureus 682 #> 3 Streptococcus pneumoniae 402 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"Conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For `aminoglycosides()` using column GEN #> (gentamicin) #> # A tibble: 2,724 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC #> (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,714 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,714 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For `aminoglycosides()` using column GEN #> (gentamicin) #> # A tibble: 981 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 971 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC #> (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 452 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"Conduct AMR data analysis","text":"AMR package supports 28 different languages antibiograms provides four types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373): Traditional Antibiogram (TA) – susceptibility species individual antibiotics Combination Antibiogram (CA) – susceptibility species combination regimens Syndromic Antibiogram (SA) – susceptibility species, stratified clinical syndrome setting Weighted-Incidence Syndromic Combination Antibiogram (WISCA) – estimated empirical coverage regimen syndrome, weighted pathogen incidence quantified uncertainty goal guide empirical therapy, WISCA default. reason simple: start empirical treatment, know pathogen causing infection. next patient present species label attached . matters probability regimen choose cover whatever pathogen turns cause, given local epidemiology syndrome. Traditional antibiograms answer question. fragment information species, ignore frequently species causes syndrome, evaluate combination regimens, provide measure uncertainty. WISCA addresses limitations using Bayesian framework (Hebert et al., 2012; Bielicki et al., 2016). See WISCA vignette full explanation. Traditional, combination, syndromic antibiograms remain useful surveillance purposes, .e., tracking resistance trends per species time. care clinical impact, choosing right empirical regimen patient, use WISCA. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"wisca-recommended-for-empirical-therapy-guidance","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"WISCA (recommended for empirical therapy guidance)","title":"Conduct AMR data analysis","text":"Use wisca() function, equivalently antibiogram(..., wisca = TRUE). WISCA produces single coverage estimate per regimen entire syndrome, weighted pathogen incidence, 95% credible interval Bayesian Monte Carlo simulation: output tells : “given species distribution data, estimated X% probability regimen covers infection, 95% credible interval [lower, upper]”. clinically relevant question. syndrome-specific patient-specific WISCA, use syndromic_group argument group data first. can stratify anything: ward, age group, risk profile, acquisition type. syndromic_group argument accepts column expression: Keep mind granular stratification produces relevant estimates subgroup, wider credible intervals due smaller sample sizes. always trade-granularity precision. local numbers small, consider pooling data multiple sites (Bielicki et al., 2016). reliable WISCA results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), since rare contaminants can distort coverage estimates. creating WISCA model, assessments can done distributions Monte Carlo simulations WISCA carried :","code":"wisca_result <- example_isolates %>% wisca( antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10 ) # Recommended threshold: ≥30 wisca_result wisca_out <- example_isolates %>% top_n_microorganisms(n = 10) %>% group_by( age_group = age_groups(age, c(25, 50, 75)), gender ) %>% wisca(antimicrobials = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) wisca_out wisca_plot(wisca_out) wisca_plot(wisca_out, wisca_plot_type = \"posterior_coverage\") # a ggplot2 extension for WISCAs and other antibiograms: ggplot2::autoplot(wisca_out)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"Conduct AMR data analysis","text":"need per-species susceptibility rates, e.g., AMR surveillance reports, traditional antibiogram remains right tool. reports proportion susceptible isolates per species per antibiotic: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> (amikacin), and KAN (kanamycin) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\" ) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> (amikacin), and KAN (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combination-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combination Antibiogram","title":"Conduct AMR data analysis","text":"combination antibiogram shows much additional susceptibility second agent adds given species. useful surveillance combination regimens, note still species-stratified account pathogen incidence syndrome:","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL ) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"Conduct AMR data analysis","text":"syndromic antibiogram stratifies per-species susceptibility clinical context (ward, specimen type, etc.). adds clinical context traditional antibiogram still species-level, without incidence weighting uncertainty quantification. surveillance setting fine; empirical therapy guidance, WISCA preferred:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\" ) #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK #> (amikacin), and KAN (kanamycin) #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"Conduct AMR data analysis","text":"antibiogram types, including WISCA, can plotted using autoplot() ggplot2 package, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(wisca_result)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"Conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> option to either \"CLSI\" or \"EUCAST\", see `?AMR-options`. #> ℹ This message will be shown once per session. #> [1] 0.4203377 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"Conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"Conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd June 2026","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot( my_data, aes(x = group, y = MIC, colour = SIR) ) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs( title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\" ) autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN
Planned as v3.1.0, end of June 2026.
domain
microorganisms
mo_kingdom()
"Pseudomonadati"
"Bacteria"
mo_domain()
kingdom
future
as.sir()
wisca()
future::plan(future::multisession)
parallel = TRUE
clinical_breakpoints
breakpoint_type = "ECOFF"
wisca_plot()