language
#> Groups (n=4, named as 'order'): diff --git a/articles/PCA.md b/articles/PCA.md index 7cbb2cf45..741cc4899 100644 --- a/articles/PCA.md +++ b/articles/PCA.md @@ -113,7 +113,7 @@ 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 5448d1a79..1bbc11aa6 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/articles/WISCA.html b/articles/WISCA.html index 88b89154d..11769d7ad 100644 --- a/articles/WISCA.html +++ b/articles/WISCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -363,14 +363,14 @@ all be used for the wisca() - + -Grupo sindrómico -Amoxicillin/ácido clavulánico -Amoxicillin/ácido clavulánico + Ciprofloxacin -Amoxicillin/ácido clavulánico + Gentamicina +Syndromic Grupo +Amoxicilina/clavulanic acid +Amoxicilina/clavulanic acid + Ciprofloxacina +Amoxicilina/clavulanic acid + Gentamicina diff --git a/articles/WISCA.md b/articles/WISCA.md index 543985664..33f6c022b 100644 --- a/articles/WISCA.md +++ b/articles/WISCA.md @@ -207,10 +207,10 @@ wisca(data, language = "Spanish") ``` -| Grupo sindrómico | Amoxicillin/ácido clavulánico | Amoxicillin/ácido clavulánico + Ciprofloxacin | Amoxicillin/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%) | +| 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%) | ## Sensible defaults, which can be customised diff --git a/articles/datasets.html b/articles/datasets.html index 6f56dbc71..c9572f0cc 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/articles/index.html b/articles/index.html index e1be814c8..d1b39c78d 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/authors.html b/authors.html index df06a8fea..2303efc12 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/index.html b/index.html index 343f6ebe8..fb40bea4a 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/news/index.html b/news/index.html index 9fa87f8b3..888d7f63e 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -49,9 +49,9 @@ -AMR 3.0.1.9012 +AMR 3.0.1.9013 -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 8057761a7..cbafd20d2 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9012 +## AMR 3.0.1.9013 #### New diff --git a/pkgdown.yml b/pkgdown.yml index c7dcdeda7..3d4aab517 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-01-07T10:04Z +last_built: 2026-01-07T12:35Z 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 b604a6f49..6d2496a1c 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index e57c1185a..eaf63f14a 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/AMR.html b/reference/AMR.html index 7c9c4f9d5..91ebfbee1 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index bfbff50f5..576e0e849 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/WHONET.html b/reference/WHONET.html index 7730eee84..166e39efa 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 838ba0c1b..cc7b5f4ae 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ab_property.html b/reference/ab_property.html index c4ab926ce..0e7cc360b 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 110bedb36..3b4eb4396 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 8a985505f..235a28250 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/age.html b/reference/age.html index b0050f9c7..c54fcb7c3 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/age_groups.html b/reference/age_groups.html index 87684db06..7caeaf8a1 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index d4ba7b9b0..7e8b4efb7 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index 883a6618e..e5b142fb5 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) - 3.0.1.9012 + 3.0.1.9013 @@ -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 -#> `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… +#> `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… #> # 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 86242c681..3600ed956 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 -#> `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… +#> `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… #> # 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 0ffdd60d3..80b302e37 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index 57e520923..d2f3c2f32 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.ab.html b/reference/as.ab.html index 57e74b5a7..b8901a747 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.av.html b/reference/as.av.html index e7c374b89..75bf80fb9 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.disk.html b/reference/as.disk.html index 2798a4538..1f1c5aea9 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png index 929e499e0..ebb4cd01f 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 36d2a5ee2..168fbb000 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.mo.html b/reference/as.mo.html index 14dcf2ed0..f953b2286 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.sir.html b/reference/as.sir.html index cacb1618f..8f22e4042 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) - 3.0.1.9012 + 3.0.1.9013 @@ -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 10:05:32 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 10:05:32 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 10:05:32 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 10:05:33 1 DISK genta Escherich… human 18 +#> 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 #> # 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 9c965d370..380606384 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 10:05:32 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 10:05:32 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 10:05:32 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 10:05:33 1 DISK genta Escherich… human 18 +#> 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 #> # 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 60c173c06..dc8a0e00f 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 4f5029bc7..5ebf7d346 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/av_property.html b/reference/av_property.html index 1092670ff..545208ca1 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/availability.html b/reference/availability.html index ec26d45fd..7c2d3b701 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index 94c361b43..6e5255e9a 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 3666c336b..c454c4b96 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/count.html b/reference/count.html index 7cc63525a..728913dd4 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index bd68ee5bf..6ea5597a1 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index 5d4dbe647..e851c4cf5 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/dosage.html b/reference/dosage.html index 92f2df5ed..d601e5654 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index f0a7d309b..523585fbe 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index f584b7a85..824647ef9 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 44396f9db..875358660 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index a7fe1ed9b..e6a2ac316 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index d46561479..a4916b76b 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 87fb708bb..f80213f89 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/g.test.html b/reference/g.test.html index dc26a6314..c10929734 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/get_episode.html b/reference/get_episode.html index 2784dd46a..35733141c 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 49400284a..1f53cf155 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 0c1ef76f8..0d5f6991b 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index d7c5db46c..367c8d7e0 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/index.html b/reference/index.html index bb629bf7a..817317465 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index 85547184e..45a214588 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index ed1f9330f..e24750d24 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/join.html b/reference/join.html index 782ce3a2f..38600e0d8 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 795298fa2..8179d8bda 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index cf97640e5..be8cada3d 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/like.html b/reference/like.html index bcc08b737..e3232d2b4 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mdro.html b/reference/mdro.html index fae588853..7ce404b50 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 953141e1d..f042045b6 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 186e5c7c7..47d177025 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index 55a5ea184..6a807de44 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index 159727a7a..f1cefbafd 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 195e86d06..d09c4e0ec 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mo_property.html b/reference/mo_property.html index 0caf5e4b6..01d78c4b5 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -428,15 +428,15 @@ # language support -------------------------------------------------------- mo_gramstain("Klebsiella pneumoniae", language = "de") # German -#> [1] "Gram-negative" +#> [1] "Gramnegativ" mo_gramstain("Klebsiella pneumoniae", language = "nl") # Dutch -#> [1] "Gram-negative" +#> [1] "Gram-negatief" mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish -#> [1] "Gram-negative" +#> [1] "Gram negativo" mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek -#> [1] "Gram-negative" +#> [1] "Αρνητικό κατά Gram" mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian -#> [1] "Gram-negative" +#> [1] "Грамнегативні" # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom("Klebsiella pneumoniae") @@ -446,7 +446,7 @@ mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect #> [1] "Bacteria" mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated -#> [1] "Bacteria" +#> [1] "细菌" mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de") #> [1] "Streptococcus Gruppe A" diff --git a/reference/mo_property.md b/reference/mo_property.md index 08390371e..d2226769f 100644 --- a/reference/mo_property.md +++ b/reference/mo_property.md @@ -542,15 +542,15 @@ mo_shortname("Strep agalactiae", Lancefield = TRUE) # language support -------------------------------------------------------- mo_gramstain("Klebsiella pneumoniae", language = "de") # German -#> [1] "Gram-negative" +#> [1] "Gramnegativ" mo_gramstain("Klebsiella pneumoniae", language = "nl") # Dutch -#> [1] "Gram-negative" +#> [1] "Gram-negatief" mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish -#> [1] "Gram-negative" +#> [1] "Gram negativo" mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek -#> [1] "Gram-negative" +#> [1] "Αρνητικό κατά Gram" mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian -#> [1] "Gram-negative" +#> [1] "Грамнегативні" # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom("Klebsiella pneumoniae") @@ -560,7 +560,7 @@ mo_type("Klebsiella pneumoniae") mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect #> [1] "Bacteria" mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated -#> [1] "Bacteria" +#> [1] "细菌" mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de") #> [1] "Streptococcus Gruppe A" diff --git a/reference/mo_source.html b/reference/mo_source.html index f9776745d..e6e628eff 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/pca.html b/reference/pca.html index f3dfcdceb..9f753d738 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/plot-13.png b/reference/plot-13.png index 429359f3a..4ec8451ce 100644 Binary files a/reference/plot-13.png and b/reference/plot-13.png differ diff --git a/reference/plot-18.png b/reference/plot-18.png index 323e12e7c..26518448c 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 a7dea2030..8b0e62016 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 e0b069603..a623784f7 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/proportion.html b/reference/proportion.html index b1bed3efa..69828e543 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/random.html b/reference/random.html index f86655b57..ea39d50e9 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 2e284215c..1953fada6 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/skewness.html b/reference/skewness.html index 63f6d29ed..b5f36cdc1 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index 0866c6070..1b378255d 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/translate.html b/reference/translate.html index 4b0c0f23f..d0234b04a 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -110,7 +110,7 @@ set_AMR_locale("Dutch") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name("Ciprofloxacin") -#> [1] "Ciprofloxacin" +#> [1] "Ciprofloxacine" mo_name("Coagulase-negative Staphylococcus (CoNS)") #> [1] "Coagulase-negatieve Staphylococcus (CNS)" diff --git a/reference/translate.md b/reference/translate.md index 884bb9029..b6ea482b7 100644 --- a/reference/translate.md +++ b/reference/translate.md @@ -101,7 +101,7 @@ mo_name("Coagulase-negative Staphylococcus (CoNS)") set_AMR_locale("Dutch") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name("Ciprofloxacin") -#> [1] "Ciprofloxacin" +#> [1] "Ciprofloxacine" mo_name("Coagulase-negative Staphylococcus (CoNS)") #> [1] "Coagulase-negatieve Staphylococcus (CNS)" diff --git a/search.json b/search.json index 0a71a84bb..6ae9c66d5 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
wisca() - + -Grupo sindrómico -Amoxicillin/ácido clavulánico -Amoxicillin/ácido clavulánico + Ciprofloxacin -Amoxicillin/ácido clavulánico + Gentamicina +Syndromic Grupo +Amoxicilina/clavulanic acid +Amoxicilina/clavulanic acid + Ciprofloxacina +Amoxicilina/clavulanic acid + Gentamicina diff --git a/articles/WISCA.md b/articles/WISCA.md index 543985664..33f6c022b 100644 --- a/articles/WISCA.md +++ b/articles/WISCA.md @@ -207,10 +207,10 @@ wisca(data, language = "Spanish") ``` -| Grupo sindrómico | Amoxicillin/ácido clavulánico | Amoxicillin/ácido clavulánico + Ciprofloxacin | Amoxicillin/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%) | +| 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%) | ## Sensible defaults, which can be customised diff --git a/articles/datasets.html b/articles/datasets.html index 6f56dbc71..c9572f0cc 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/articles/index.html b/articles/index.html index e1be814c8..d1b39c78d 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/authors.html b/authors.html index df06a8fea..2303efc12 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/index.html b/index.html index 343f6ebe8..fb40bea4a 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/news/index.html b/news/index.html index 9fa87f8b3..888d7f63e 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -49,9 +49,9 @@ -AMR 3.0.1.9012 +AMR 3.0.1.9013 -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 8057761a7..cbafd20d2 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9012 +## AMR 3.0.1.9013 #### New diff --git a/pkgdown.yml b/pkgdown.yml index c7dcdeda7..3d4aab517 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-01-07T10:04Z +last_built: 2026-01-07T12:35Z 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 b604a6f49..6d2496a1c 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index e57c1185a..eaf63f14a 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/AMR.html b/reference/AMR.html index 7c9c4f9d5..91ebfbee1 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index bfbff50f5..576e0e849 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/WHONET.html b/reference/WHONET.html index 7730eee84..166e39efa 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 838ba0c1b..cc7b5f4ae 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ab_property.html b/reference/ab_property.html index c4ab926ce..0e7cc360b 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 110bedb36..3b4eb4396 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index 8a985505f..235a28250 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/age.html b/reference/age.html index b0050f9c7..c54fcb7c3 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/age_groups.html b/reference/age_groups.html index 87684db06..7caeaf8a1 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index d4ba7b9b0..7e8b4efb7 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index 883a6618e..e5b142fb5 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) - 3.0.1.9012 + 3.0.1.9013 @@ -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 -#> `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… +#> `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… #> # 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 86242c681..3600ed956 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 -#> `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… +#> `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… #> # 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 0ffdd60d3..80b302e37 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index 57e520923..d2f3c2f32 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.ab.html b/reference/as.ab.html index 57e74b5a7..b8901a747 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.av.html b/reference/as.av.html index e7c374b89..75bf80fb9 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.disk.html b/reference/as.disk.html index 2798a4538..1f1c5aea9 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.mic-4.png b/reference/as.mic-4.png index 929e499e0..ebb4cd01f 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 36d2a5ee2..168fbb000 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.mo.html b/reference/as.mo.html index 14dcf2ed0..f953b2286 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/as.sir.html b/reference/as.sir.html index cacb1618f..8f22e4042 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) - 3.0.1.9012 + 3.0.1.9013 @@ -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 10:05:32 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 10:05:32 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 10:05:32 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 10:05:33 1 DISK genta Escherich… human 18 +#> 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 #> # 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 9c965d370..380606384 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 10:05:32 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-07 10:05:32 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-07 10:05:32 1 DISK tobra Escherich… human 16 -#> 4 2026-01-07 10:05:33 1 DISK genta Escherich… human 18 +#> 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 #> # 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 60c173c06..dc8a0e00f 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 4f5029bc7..5ebf7d346 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/av_property.html b/reference/av_property.html index 1092670ff..545208ca1 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/availability.html b/reference/availability.html index ec26d45fd..7c2d3b701 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index 94c361b43..6e5255e9a 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 3666c336b..c454c4b96 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/count.html b/reference/count.html index 7cc63525a..728913dd4 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index bd68ee5bf..6ea5597a1 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index 5d4dbe647..e851c4cf5 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/dosage.html b/reference/dosage.html index 92f2df5ed..d601e5654 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index f0a7d309b..523585fbe 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index f584b7a85..824647ef9 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 44396f9db..875358660 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index a7fe1ed9b..e6a2ac316 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index d46561479..a4916b76b 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 87fb708bb..f80213f89 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/g.test.html b/reference/g.test.html index dc26a6314..c10929734 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/get_episode.html b/reference/get_episode.html index 2784dd46a..35733141c 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 49400284a..1f53cf155 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 0c1ef76f8..0d5f6991b 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index d7c5db46c..367c8d7e0 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/index.html b/reference/index.html index bb629bf7a..817317465 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index 85547184e..45a214588 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index ed1f9330f..e24750d24 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/join.html b/reference/join.html index 782ce3a2f..38600e0d8 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 795298fa2..8179d8bda 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index cf97640e5..be8cada3d 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/like.html b/reference/like.html index bcc08b737..e3232d2b4 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mdro.html b/reference/mdro.html index fae588853..7ce404b50 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 953141e1d..f042045b6 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 186e5c7c7..47d177025 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index 55a5ea184..6a807de44 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index 159727a7a..f1cefbafd 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index 195e86d06..d09c4e0ec 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/mo_property.html b/reference/mo_property.html index 0caf5e4b6..01d78c4b5 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -428,15 +428,15 @@ # language support -------------------------------------------------------- mo_gramstain("Klebsiella pneumoniae", language = "de") # German -#> [1] "Gram-negative" +#> [1] "Gramnegativ" mo_gramstain("Klebsiella pneumoniae", language = "nl") # Dutch -#> [1] "Gram-negative" +#> [1] "Gram-negatief" mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish -#> [1] "Gram-negative" +#> [1] "Gram negativo" mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek -#> [1] "Gram-negative" +#> [1] "Αρνητικό κατά Gram" mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian -#> [1] "Gram-negative" +#> [1] "Грамнегативні" # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom("Klebsiella pneumoniae") @@ -446,7 +446,7 @@ mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect #> [1] "Bacteria" mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated -#> [1] "Bacteria" +#> [1] "细菌" mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de") #> [1] "Streptococcus Gruppe A" diff --git a/reference/mo_property.md b/reference/mo_property.md index 08390371e..d2226769f 100644 --- a/reference/mo_property.md +++ b/reference/mo_property.md @@ -542,15 +542,15 @@ mo_shortname("Strep agalactiae", Lancefield = TRUE) # language support -------------------------------------------------------- mo_gramstain("Klebsiella pneumoniae", language = "de") # German -#> [1] "Gram-negative" +#> [1] "Gramnegativ" mo_gramstain("Klebsiella pneumoniae", language = "nl") # Dutch -#> [1] "Gram-negative" +#> [1] "Gram-negatief" mo_gramstain("Klebsiella pneumoniae", language = "es") # Spanish -#> [1] "Gram-negative" +#> [1] "Gram negativo" mo_gramstain("Klebsiella pneumoniae", language = "el") # Greek -#> [1] "Gram-negative" +#> [1] "Αρνητικό κατά Gram" mo_gramstain("Klebsiella pneumoniae", language = "uk") # Ukrainian -#> [1] "Gram-negative" +#> [1] "Грамнегативні" # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom("Klebsiella pneumoniae") @@ -560,7 +560,7 @@ mo_type("Klebsiella pneumoniae") mo_kingdom("Klebsiella pneumoniae", language = "zh") # Chinese, no effect #> [1] "Bacteria" mo_type("Klebsiella pneumoniae", language = "zh") # Chinese, translated -#> [1] "Bacteria" +#> [1] "细菌" mo_fullname("S. pyogenes", Lancefield = TRUE, language = "de") #> [1] "Streptococcus Gruppe A" diff --git a/reference/mo_source.html b/reference/mo_source.html index f9776745d..e6e628eff 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/pca.html b/reference/pca.html index f3dfcdceb..9f753d738 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/plot-13.png b/reference/plot-13.png index 429359f3a..4ec8451ce 100644 Binary files a/reference/plot-13.png and b/reference/plot-13.png differ diff --git a/reference/plot-18.png b/reference/plot-18.png index 323e12e7c..26518448c 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 a7dea2030..8b0e62016 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 e0b069603..a623784f7 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/proportion.html b/reference/proportion.html index b1bed3efa..69828e543 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/random.html b/reference/random.html index f86655b57..ea39d50e9 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 2e284215c..1953fada6 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/skewness.html b/reference/skewness.html index 63f6d29ed..b5f36cdc1 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) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index 0866c6070..1b378255d 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 diff --git a/reference/translate.html b/reference/translate.html index 4b0c0f23f..d0234b04a 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9012 + 3.0.1.9013 @@ -110,7 +110,7 @@ set_AMR_locale("Dutch") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name("Ciprofloxacin") -#> [1] "Ciprofloxacin" +#> [1] "Ciprofloxacine" mo_name("Coagulase-negative Staphylococcus (CoNS)") #> [1] "Coagulase-negatieve Staphylococcus (CNS)" diff --git a/reference/translate.md b/reference/translate.md index 884bb9029..b6ea482b7 100644 --- a/reference/translate.md +++ b/reference/translate.md @@ -101,7 +101,7 @@ mo_name("Coagulase-negative Staphylococcus (CoNS)") set_AMR_locale("Dutch") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name("Ciprofloxacin") -#> [1] "Ciprofloxacin" +#> [1] "Ciprofloxacine" mo_name("Coagulase-negative Staphylococcus (CoNS)") #> [1] "Coagulase-negatieve Staphylococcus (CNS)" diff --git a/search.json b/search.json index 0a71a84bb..6ae9c66d5 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
recipes
step_mic_log2()
<mic>
step_sir_numeric()
<sir>
ab_group()
all_groups
antibiogram()
TAN
FTA
antimicrobials
as.sir()
S
i
R