wisca() - + -Syndromic Grupo -Amoxicilina/clavulanic acid -Amoxicilina/clavulanic acid + Ciprofloxacina -Amoxicilina/clavulanic acid + Gentamicina +Grupo sindrómico +Amoxicilina/ácido clavulánico +Amoxicilina/ácido clavulánico + Ciprofloxacina +Amoxicilina/ácido clavulánico + Gentamicina diff --git a/articles/WISCA.md b/articles/WISCA.md index 33f6c022b..66a5526de 100644 --- a/articles/WISCA.md +++ b/articles/WISCA.md @@ -207,10 +207,10 @@ wisca(data, language = "Spanish") ``` -| Syndromic Grupo | Amoxicilina/clavulanic acid | Amoxicilina/clavulanic acid + Ciprofloxacina | Amoxicilina/clavulanic acid + Gentamicina | -|:----------------|:----------------------------|:---------------------------------------------|:------------------------------------------| -| No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) | -| UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) | +| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina | +|:-----------------|:------------------------------|:-----------------------------------------------|:--------------------------------------------| +| No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) | +| UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) | ## Sensible defaults, which can be customised diff --git a/articles/datasets.html b/articles/datasets.html index c9572f0cc..3c6fdecac 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/articles/index.html b/articles/index.html index d1b39c78d..8f519d643 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/authors.html b/authors.html index 2303efc12..0dde9733b 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/index.html b/index.html index fb40bea4a..6f8023f33 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/news/index.html b/news/index.html index 888d7f63e..9c8b210a0 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 @@ -49,9 +49,9 @@ -AMR 3.0.1.9013 +AMR 3.0.1.9014 -New +New Integration with the tidymodels framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via recipes step_mic_log2() to transform <mic> columns with log2, and step_sir_numeric() to convert <sir> columns to numeric @@ -74,7 +74,7 @@ ab_group() gained an argument all_groups to return all groups the antimicrobial drug is in (#246) -Changed +Changed Fixed a bug in antibiogram() for when no antimicrobials are set Added taniborbactam (TAN) and cefepime/taniborbactam (FTA) to the antimicrobials data set Fixed a bug in as.sir() where for numeric input the arguments S, i, and R would not be considered (#244) diff --git a/news/index.md b/news/index.md index cbafd20d2..4bce7d0d0 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9013 +## AMR 3.0.1.9014 #### New diff --git a/pkgdown.yml b/pkgdown.yml index 3d4aab517..831ff24da 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-01-07T12:35Z +last_built: 2026-01-07T14:13Z 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 6d2496a1c..64d132e6c 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index eaf63f14a..d10ff58db 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/AMR.html b/reference/AMR.html index 91ebfbee1..ec77c8056 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 576e0e849..098b82068 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/WHONET.html b/reference/WHONET.html index 166e39efa..43e734ea1 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index cc7b5f4ae..5a4587fea 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/ab_property.html b/reference/ab_property.html index 0e7cc360b..e395a4c5e 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 3b4eb4396..935ba12f7 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 235a28250..3855c08ed 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/age.html b/reference/age.html index c54fcb7c3..48289091e 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/age_groups.html b/reference/age_groups.html index 7caeaf8a1..64b3f3ef0 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index 7e8b4efb7..f6ccc7623 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index e5b142fb5..a7cae385b 100644 --- a/reference/antibiogram.html +++ b/reference/antibiogram.html @@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye AMR (for R) - 3.0.1.9013 + 3.0.1.9014 @@ -567,10 +567,10 @@ Adhering to previously described approaches (see Source) and especially the Baye #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram: 2 × 5 #> # Type: Non-WISCA with 95% CI -#> `Syndromic Grupo` Patógeno Amikacina Gentamicina Tobramicina -#> <chr> <chr> <chr> <chr> <chr> -#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=323) 98% (96-99… -#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=13… 96% (92-99… +#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina +#> <chr> <chr> <chr> <chr> <chr> +#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… +#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram diff --git a/reference/antibiogram.md b/reference/antibiogram.md index 3600ed956..86242c681 100644 --- a/reference/antibiogram.md +++ b/reference/antibiogram.md @@ -757,10 +757,10 @@ antibiogram(ex1, #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram: 2 × 5 #> # Type: Non-WISCA with 95% CI -#> `Syndromic Grupo` Patógeno Amikacina Gentamicina Tobramicina -#> -#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=323) 98% (96-99… -#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=13… 96% (92-99… +#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina +#> +#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… +#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99… #> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html index 80b302e37..91eb18382 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index d2f3c2f32..0c6381571 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.ab.html b/reference/as.ab.html index b8901a747..475f73b65 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.av.html b/reference/as.av.html index 75bf80fb9..f04efc8d6 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.disk.html b/reference/as.disk.html index 1f1c5aea9..fafe4118f 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png index ebb4cd01f..5255255ce 100644 Binary files a/reference/as.mic-4.png and b/reference/as.mic-4.png differ diff --git a/reference/as.mic.html b/reference/as.mic.html index 168fbb000..adf575ebf 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.mo.html b/reference/as.mo.html index f953b2286..85ec79fbd 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/as.sir.html b/reference/as.sir.html index 8f22e4042..36b107f9e 100644 --- a/reference/as.sir.html +++ b/reference/as.sir.html @@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025, AMR (for R) - 3.0.1.9013 + 3.0.1.9014 @@ -416,10 +416,10 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025, #> # A tibble: 4 × 18 #> datetime index method ab_given mo_given host_given input_given #> <dttm> <int> <chr> <chr> <chr> <chr> <chr> -#> 1 2026-01-07 12:36:45 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 12:36:46 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 12:36:46 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 12:36:46 1 DISK genta Escherich… human 18 +#> 1 2026-01-07 14:14:22 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-01-07 14:14:22 1 MIC cipro Escherich… human 0.256 +#> 3 2026-01-07 14:14:23 1 DISK tobra Escherich… human 16 +#> 4 2026-01-07 14:14:23 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 380606384..59b8e20b5 100644 --- a/reference/as.sir.md +++ b/reference/as.sir.md @@ -650,10 +650,10 @@ sir_interpretation_history() #> # A tibble: 4 × 18 #> datetime index method ab_given mo_given host_given input_given #> -#> 1 2026-01-07 12:36:45 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 12:36:46 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 12:36:46 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 12:36:46 1 DISK genta Escherich… human 18 +#> 1 2026-01-07 14:14:22 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-01-07 14:14:22 1 MIC cipro Escherich… human 0.256 +#> 3 2026-01-07 14:14:23 1 DISK tobra Escherich… human 16 +#> 4 2026-01-07 14:14:23 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 dc8a0e00f..44c25b915 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 5ebf7d346..5d70875ba 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/av_property.html b/reference/av_property.html index 545208ca1..be2203363 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/availability.html b/reference/availability.html index 7c2d3b701..09070568c 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index 6e5255e9a..f214ade61 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index c454c4b96..c2daa3f0a 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/count.html b/reference/count.html index 728913dd4..0864fd8ae 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index 6ea5597a1..99c8eeec2 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index e851c4cf5..ec6dc1483 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/dosage.html b/reference/dosage.html index d601e5654..5cf73ef44 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index 523585fbe..7791866ce 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index 824647ef9..22e99f105 100644 --- a/reference/eucast_rules.html +++ b/reference/eucast_rules.html @@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 875358660..8038767ad 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index e6a2ac316..7061f23ca 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index a4916b76b..5ee32961d 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index f80213f89..2cc107134 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/g.test.html b/reference/g.test.html index c10929734..880da4141 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/get_episode.html b/reference/get_episode.html index 35733141c..dec69ec17 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 1f53cf155..4f2cd7bb5 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 0d5f6991b..f597c1a13 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 367c8d7e0..9d6c9038c 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/index.html b/reference/index.html index 817317465..3729c2c01 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index 45a214588..4c3a3da23 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index e24750d24..919b33ace 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/join.html b/reference/join.html index 38600e0d8..94c11a95b 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 8179d8bda..a42d76693 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index be8cada3d..26f2b1373 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/like.html b/reference/like.html index e3232d2b4..8900aa863 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/mdro.html b/reference/mdro.html index 7ce404b50..ebb0cd95d 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index f042045b6..451a2a13e 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 47d177025..f28ffec56 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index 6a807de44..e8e558b1d 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index f1cefbafd..664c4922b 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index d09c4e0ec..578913bc9 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/mo_property.html b/reference/mo_property.html index 01d78c4b5..c506e4fc0 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/mo_source.html b/reference/mo_source.html index e6e628eff..9b0023c48 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/pca.html b/reference/pca.html index 9f753d738..0587b1c24 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/plot-18.png b/reference/plot-18.png index 26518448c..6e24e395e 100644 Binary files a/reference/plot-18.png and b/reference/plot-18.png differ diff --git a/reference/plot-4.png b/reference/plot-4.png index 8b0e62016..62dbd73a4 100644 Binary files a/reference/plot-4.png and b/reference/plot-4.png differ diff --git a/reference/plot.html b/reference/plot.html index a623784f7..f30fa95c5 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/proportion.html b/reference/proportion.html index 69828e543..72c5d5087 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/random.html b/reference/random.html index ea39d50e9..ecce36f1a 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 1953fada6..7f6c9d6d8 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/skewness.html b/reference/skewness.html index b5f36cdc1..ea94c2872 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) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index 1b378255d..8b4f13f58 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 diff --git a/reference/translate.html b/reference/translate.html index d0234b04a..5749faf79 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9013 + 3.0.1.9014 @@ -130,7 +130,7 @@ set_AMR_locale("de") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name("amox/clav") -#> [1] "Amoxicillin/clavulanic acid" +#> [1] "Amoxicillin/Clavulansäure" # reset to system default reset_AMR_locale() diff --git a/reference/translate.md b/reference/translate.md index b6ea482b7..4b2c07cd3 100644 --- a/reference/translate.md +++ b/reference/translate.md @@ -121,7 +121,7 @@ set_AMR_locale("German") set_AMR_locale("de") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name("amox/clav") -#> [1] "Amoxicillin/clavulanic acid" +#> [1] "Amoxicillin/Clavulansäure" # reset to system default reset_AMR_locale() diff --git a/search.json b/search.json index 6ae9c66d5..ff1c56c80 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial drugs, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"Conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"Conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"Conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the `microorganisms.codes` data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> `mo_uncertainties()` to review these uncertainties, or use #> `add_custom_microorganisms()` to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella #> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), #> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii #> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> Only the first 10 other matches of each record are shown. Run #> `print(mo_uncertainties(), n = ...)` to view more entries, or save #> `mo_uncertainties()` to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"Conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"Conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 730 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for `col_mo`. #> ℹ Using column 'date' as input for `col_date`. #> ℹ Using column 'patient_id' as input for `col_patient_id`. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,730 'phenotype-based' first isolates (91.0% 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: 2,730 × 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 2,720 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"Conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2730 Length:2730 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-06 #> Mode :character Mode :character Median :2015-06-04 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-14 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :40.1% (n=1071) %S :51.1% (n=1354) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :17.0% (n=453) %I :12.7% (n=335) #> #2 :B_STPHY_AURS %R :42.9% (n=1147) %R :36.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.2% (n=1426) %S :60.7% (n=1656) TRUE:2730 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=178) %I : 3.0% (n=83) #> %R :41.2% (n=1126) %R :36.3% (n=991) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,730 #> 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 4 4 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 1326 #> 2 Staphylococcus aureus 684 #> 3 Streptococcus pneumoniae 401 #> 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: 2,730 × 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 2,720 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,730 × 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 2,720 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,730 × 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 2,720 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: 991 × 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 981 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 461 × 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 451 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: 461 × 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 451 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"Conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"Conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"Conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"Conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"Conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"Conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"Conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4294272 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.341 #> 2 B 0.586 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"Conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"Conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX
recipes
step_mic_log2()
<mic>
step_sir_numeric()
<sir>
ab_group()
all_groups
antibiogram()
TAN
FTA
antimicrobials
as.sir()
S
i
R