From 86f19c7576e5b4df9dcc02b2a3bae93c62ec4c52 Mon Sep 17 00:00:00 2001 From: github-actions <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 8 Jan 2026 13:12:14 +0000 Subject: [PATCH] Built site for AMR@3.0.1.9017: fd50c51 --- 404.html | 2 +- LICENSE-text.html | 2 +- articles/AMR.html | 2 +- articles/AMR_for_Python.html | 2 +- articles/AMR_with_tidymodels.html | 2 +- articles/EUCAST.html | 2 +- articles/PCA.html | 2 +- articles/WHONET.html | 2 +- articles/WISCA.html | 2 +- articles/datasets.html | 2 +- articles/index.html | 2 +- authors.html | 2 +- index.html | 2 +- news/index.html | 8 ++++---- news/index.md | 2 +- pkgdown.yml | 2 +- reference/AMR-deprecated.html | 2 +- reference/AMR-options.html | 2 +- reference/AMR.html | 2 +- reference/WHOCC.html | 2 +- reference/WHONET.html | 2 +- reference/ab_from_text.html | 2 +- reference/ab_property.html | 6 +++++- reference/ab_property.md | 10 ++++++++++ reference/add_custom_antimicrobials.html | 2 +- reference/add_custom_microorganisms.html | 2 +- reference/age.html | 2 +- reference/age_groups.html | 2 +- reference/amr-tidymodels.html | 2 +- reference/antibiogram.html | 2 +- reference/antimicrobial_selectors.html | 2 +- reference/antimicrobials.html | 2 +- reference/as.ab.html | 2 +- reference/as.av.html | 2 +- reference/as.disk.html | 2 +- reference/as.mic.html | 2 +- reference/as.mo.html | 2 +- reference/as.sir.html | 10 +++++----- reference/as.sir.md | 8 ++++---- reference/atc_online.html | 2 +- reference/av_from_text.html | 2 +- reference/av_property.html | 2 +- reference/availability.html | 2 +- reference/bug_drug_combinations.html | 2 +- reference/clinical_breakpoints.html | 2 +- reference/count.html | 2 +- reference/custom_eucast_rules.html | 2 +- reference/custom_mdro_guideline.html | 2 +- reference/dosage.html | 2 +- reference/esbl_isolates.html | 2 +- reference/eucast_rules.html | 2 +- reference/example_isolates.html | 2 +- reference/example_isolates_unclean.html | 2 +- reference/export_ncbi_biosample.html | 2 +- reference/first_isolate.html | 2 +- reference/g.test.html | 2 +- reference/get_episode.html | 2 +- reference/ggplot_pca.html | 2 +- reference/ggplot_sir.html | 2 +- reference/guess_ab_col.html | 2 +- reference/index.html | 2 +- reference/intrinsic_resistant.html | 2 +- reference/italicise_taxonomy.html | 2 +- reference/join.html | 2 +- reference/key_antimicrobials.html | 2 +- reference/kurtosis.html | 2 +- reference/like.html | 2 +- reference/mdro.html | 2 +- reference/mean_amr_distance.html | 2 +- reference/microorganisms.codes.html | 2 +- reference/microorganisms.groups.html | 2 +- reference/microorganisms.html | 2 +- reference/mo_matching_score.html | 2 +- reference/mo_property.html | 2 +- reference/mo_source.html | 2 +- reference/pca.html | 2 +- reference/plot.html | 2 +- reference/proportion.html | 2 +- reference/random.html | 2 +- reference/resistance_predict.html | 2 +- reference/skewness.html | 2 +- reference/top_n_microorganisms.html | 2 +- reference/translate.html | 2 +- search.json | 2 +- 84 files changed, 107 insertions(+), 93 deletions(-) diff --git a/404.html b/404.html index f1b2061c4..d71b00313 100644 --- a/404.html +++ b/404.html @@ -31,7 +31,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/LICENSE-text.html b/LICENSE-text.html index 8e7b8a698..03a28c5b8 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/AMR.html b/articles/AMR.html index 6eaff6cbe..08e071e5b 100644 --- a/articles/AMR.html +++ b/articles/AMR.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html index b48d07776..f70b90464 100644 --- a/articles/AMR_for_Python.html +++ b/articles/AMR_for_Python.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html index 600afc751..09399451c 100644 --- a/articles/AMR_with_tidymodels.html +++ b/articles/AMR_with_tidymodels.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/EUCAST.html b/articles/EUCAST.html index 4a5709383..d5a6d6140 100644 --- a/articles/EUCAST.html +++ b/articles/EUCAST.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/PCA.html b/articles/PCA.html index e7380655e..39c5d8f01 100644 --- a/articles/PCA.html +++ b/articles/PCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/WHONET.html b/articles/WHONET.html index be57ecdb7..ce9db5e84 100644 --- a/articles/WHONET.html +++ b/articles/WHONET.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/WISCA.html b/articles/WISCA.html index e03ac093c..5449ef804 100644 --- a/articles/WISCA.html +++ b/articles/WISCA.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/datasets.html b/articles/datasets.html index 4311bc55e..7c6970643 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -30,7 +30,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/articles/index.html b/articles/index.html index 339cc8f0d..e58416c7f 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/authors.html b/authors.html index 7f31ca311..bccfaac1f 100644 --- a/authors.html +++ b/authors.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/index.html b/index.html index 8f1f42c39..8e3e9a63e 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/news/index.html b/news/index.html index 14d48e7c2..a565dc453 100644 --- a/news/index.html +++ b/news/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 @@ -49,9 +49,9 @@ -AMR 3.0.1.9016 +AMR 3.0.1.9017 -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 3be5fe91a..2076bec76 100644 --- a/news/index.md +++ b/news/index.md @@ -1,6 +1,6 @@ # Changelog -## AMR 3.0.1.9016 +## AMR 3.0.1.9017 #### New diff --git a/pkgdown.yml b/pkgdown.yml index 4e5350e7e..b2bd1881a 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -10,7 +10,7 @@ articles: PCA: PCA.html WHONET: WHONET.html WISCA: WISCA.html -last_built: 2026-01-08T11:32Z +last_built: 2026-01-08T13:07Z 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 fd3056c85..e49ea5816 100644 --- a/reference/AMR-deprecated.html +++ b/reference/AMR-deprecated.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/AMR-options.html b/reference/AMR-options.html index 7fadd05f8..0f7a9ca70 100644 --- a/reference/AMR-options.html +++ b/reference/AMR-options.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/AMR.html b/reference/AMR.html index 7303b3e19..cb3e6acd0 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/WHOCC.html b/reference/WHOCC.html index 7e24d46e2..107f39e0c 100644 --- a/reference/WHOCC.html +++ b/reference/WHOCC.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/WHONET.html b/reference/WHONET.html index c791dc4a2..cc790bc16 100644 --- a/reference/WHONET.html +++ b/reference/WHONET.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html index 07754ba3e..045c32b16 100644 --- a/reference/ab_from_text.html +++ b/reference/ab_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/ab_property.html b/reference/ab_property.html index 5bb7939bc..bff8a2f1c 100644 --- a/reference/ab_property.html +++ b/reference/ab_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 @@ -107,6 +107,10 @@ In case of set_ab_names() and data is a data.frame: columns to select (supports tidy selection such as column1:column4), otherwise other arguments passed on to as.ab(). +all_groups +A logical to indicate whether all antimicrobial groups must be return as a vector for each input value. For example, an antibiotic in the "aminopenicillins" group, is also in the "penicillins" and "beta-lactams" groups. Setting all_groups = TRUE would return all three for such an antibiotic, while all_groups = FALSE (default) only returns the most distinctive group name. + + only_first A logical to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group). diff --git a/reference/ab_property.md b/reference/ab_property.md index 0db802a4d..f489eef93 100644 --- a/reference/ab_property.md +++ b/reference/ab_property.md @@ -72,6 +72,16 @@ set_ab_names(data, ..., property = "name", language = get_AMR_locale(), other arguments passed on to [`as.ab()`](https://amr-for-r.org/reference/as.ab.md). +- all_groups: + + A [logical](https://rdrr.io/r/base/logical.html) to indicate whether + all antimicrobial groups must be return as a vector for each input + value. For example, an antibiotic in the "aminopenicillins" group, is + also in the "penicillins" and "beta-lactams" groups. Setting + `all_groups = TRUE` would return all three for such an antibiotic, + while `all_groups = FALSE` (default) only returns the most distinctive + group name. + - only_first: A [logical](https://rdrr.io/r/base/logical.html) to indicate whether diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html index 358288efd..fa2006fb1 100644 --- a/reference/add_custom_antimicrobials.html +++ b/reference/add_custom_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html index ec4bf70c6..241f6f7f0 100644 --- a/reference/add_custom_microorganisms.html +++ b/reference/add_custom_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/age.html b/reference/age.html index faafa66ba..db6e2b649 100644 --- a/reference/age.html +++ b/reference/age.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/age_groups.html b/reference/age_groups.html index 89edbf9c3..5c1012bb1 100644 --- a/reference/age_groups.html +++ b/reference/age_groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/amr-tidymodels.html b/reference/amr-tidymodels.html index c5759048f..801f7f91c 100644 --- a/reference/amr-tidymodels.html +++ b/reference/amr-tidymodels.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/antibiogram.html b/reference/antibiogram.html index ee0aa715c..827e04b7b 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html index 86eb76334..bf3f77b40 100644 --- a/reference/antimicrobial_selectors.html +++ b/reference/antimicrobial_selectors.html @@ -17,7 +17,7 @@ my_data_with_all_these_columns %>% AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html index a2da06327..dfb0c7dd5 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.ab.html b/reference/as.ab.html index 7fdf733ff..0502ecee3 100644 --- a/reference/as.ab.html +++ b/reference/as.ab.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.av.html b/reference/as.av.html index 7a0660a4e..e7b509cd9 100644 --- a/reference/as.av.html +++ b/reference/as.av.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.disk.html b/reference/as.disk.html index 0f2b4badc..15f560d65 100644 --- a/reference/as.disk.html +++ b/reference/as.disk.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.mic.html b/reference/as.mic.html index d7ba595ca..63715998d 100644 --- a/reference/as.mic.html +++ b/reference/as.mic.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.mo.html b/reference/as.mo.html index 347caf341..7dda019b2 100644 --- a/reference/as.mo.html +++ b/reference/as.mo.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/as.sir.html b/reference/as.sir.html index 1724d9fa8..d66cc0002 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) - 3.0.1.9016 + 3.0.1.9017 @@ -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 2026-01-08 11:33:03 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-08 11:33:03 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-08 11:33:04 1 DISK tobra Escherich… human 16 -#> 4 2026-01-08 11:33:04 1 DISK genta Escherich… human 18 +#> 1 2026-01-08 13:08:47 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-01-08 13:08:47 1 MIC cipro Escherich… human 0.256 +#> 3 2026-01-08 13:08:48 1 DISK tobra Escherich… human 16 +#> 4 2026-01-08 13:08:48 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 0ab65b5f0..9bdb74d68 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 2026-01-08 11:33:03 1 MIC amoxicillin Escherich… human 8 -#> 2 2026-01-08 11:33:03 1 MIC cipro Escherich… human 0.256 -#> 3 2026-01-08 11:33:04 1 DISK tobra Escherich… human 16 -#> 4 2026-01-08 11:33:04 1 DISK genta Escherich… human 18 +#> 1 2026-01-08 13:08:47 1 MIC amoxicillin Escherich… human 8 +#> 2 2026-01-08 13:08:47 1 MIC cipro Escherich… human 0.256 +#> 3 2026-01-08 13:08:48 1 DISK tobra Escherich… human 16 +#> 4 2026-01-08 13:08:48 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 f0092fdbc..288a310c8 100644 --- a/reference/atc_online.html +++ b/reference/atc_online.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/av_from_text.html b/reference/av_from_text.html index 98fdfa490..7e1563cd9 100644 --- a/reference/av_from_text.html +++ b/reference/av_from_text.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/av_property.html b/reference/av_property.html index 5d8dc9e5b..ed96ea799 100644 --- a/reference/av_property.html +++ b/reference/av_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/availability.html b/reference/availability.html index bf2d665d2..35d464b2b 100644 --- a/reference/availability.html +++ b/reference/availability.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html index ea2a161ab..e14b86dd7 100644 --- a/reference/bug_drug_combinations.html +++ b/reference/bug_drug_combinations.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html index 4f0e1904d..bf051f12a 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/count.html b/reference/count.html index 5c860771f..704ce6879 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html index 4617c8df3..bfb5c4c92 100644 --- a/reference/custom_eucast_rules.html +++ b/reference/custom_eucast_rules.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html index dcac1d274..c88f29208 100644 --- a/reference/custom_mdro_guideline.html +++ b/reference/custom_mdro_guideline.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/dosage.html b/reference/dosage.html index 6b67546d2..114fb5b5f 100644 --- a/reference/dosage.html +++ b/reference/dosage.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html index e9edda9d6..d4c737a51 100644 --- a/reference/esbl_isolates.html +++ b/reference/esbl_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html index 415bdbe97..e3625a6f3 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/example_isolates.html b/reference/example_isolates.html index 8b0e24bcd..c0c0ce870 100644 --- a/reference/example_isolates.html +++ b/reference/example_isolates.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html index 5a82e3662..1b704ed38 100644 --- a/reference/example_isolates_unclean.html +++ b/reference/example_isolates_unclean.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html index 152ab5b76..9f6154292 100644 --- a/reference/export_ncbi_biosample.html +++ b/reference/export_ncbi_biosample.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/first_isolate.html b/reference/first_isolate.html index 4d5dc7f97..861f5710f 100644 --- a/reference/first_isolate.html +++ b/reference/first_isolate.html @@ -9,7 +9,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/g.test.html b/reference/g.test.html index 8a51415f8..6dbabb4e6 100644 --- a/reference/g.test.html +++ b/reference/g.test.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/get_episode.html b/reference/get_episode.html index dffe0b84c..d046a4ea6 100644 --- a/reference/get_episode.html +++ b/reference/get_episode.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html index 29af16d08..a8a927732 100644 --- a/reference/ggplot_pca.html +++ b/reference/ggplot_pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html index 502366acc..1acff8aea 100644 --- a/reference/ggplot_sir.html +++ b/reference/ggplot_sir.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html index 7da9ec5b9..b66483fb8 100644 --- a/reference/guess_ab_col.html +++ b/reference/guess_ab_col.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/index.html b/reference/index.html index 0dbc12587..3ab28c86a 100644 --- a/reference/index.html +++ b/reference/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html index ca2eddcba..6a312814c 100644 --- a/reference/intrinsic_resistant.html +++ b/reference/intrinsic_resistant.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html index 7f93f6eef..987a0d11c 100644 --- a/reference/italicise_taxonomy.html +++ b/reference/italicise_taxonomy.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/join.html b/reference/join.html index 259eaa41c..eaf56c155 100644 --- a/reference/join.html +++ b/reference/join.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html index 38d013ae7..1bcfb9d5c 100644 --- a/reference/key_antimicrobials.html +++ b/reference/key_antimicrobials.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/kurtosis.html b/reference/kurtosis.html index 93d493489..e793ed24e 100644 --- a/reference/kurtosis.html +++ b/reference/kurtosis.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/like.html b/reference/like.html index 8bdbae4d1..ca4dfa706 100644 --- a/reference/like.html +++ b/reference/like.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/mdro.html b/reference/mdro.html index e10653875..b559b06d3 100644 --- a/reference/mdro.html +++ b/reference/mdro.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html index 3da64f553..00a862aa2 100644 --- a/reference/mean_amr_distance.html +++ b/reference/mean_amr_distance.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html index 9a8c9e6fa..40750d9d2 100644 --- a/reference/microorganisms.codes.html +++ b/reference/microorganisms.codes.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html index d4388acd5..d9e0e9597 100644 --- a/reference/microorganisms.groups.html +++ b/reference/microorganisms.groups.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/microorganisms.html b/reference/microorganisms.html index 9d3fa34f4..7bb6108b8 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html index c5eb8d04e..02c9f6287 100644 --- a/reference/mo_matching_score.html +++ b/reference/mo_matching_score.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/mo_property.html b/reference/mo_property.html index 600dc6469..206e72c7e 100644 --- a/reference/mo_property.html +++ b/reference/mo_property.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/mo_source.html b/reference/mo_source.html index d09310d91..08759f868 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/pca.html b/reference/pca.html index aeda363dc..ac6e07590 100644 --- a/reference/pca.html +++ b/reference/pca.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/plot.html b/reference/plot.html index 27b49b7a6..badef4222 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/proportion.html b/reference/proportion.html index e2918d1dc..26d357421 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/random.html b/reference/random.html index 5b30262c9..df2c7d68f 100644 --- a/reference/random.html +++ b/reference/random.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html index 6e89bf790..7bc6ef373 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/skewness.html b/reference/skewness.html index 04e150765..6a4c85c1a 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) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html index aa355358c..a50104813 100644 --- a/reference/top_n_microorganisms.html +++ b/reference/top_n_microorganisms.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/reference/translate.html b/reference/translate.html index 1f75f9f84..43e19d891 100644 --- a/reference/translate.html +++ b/reference/translate.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9016 + 3.0.1.9017 diff --git a/search.json b/search.json index d0403d7aa..de5b37e14 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial drugs, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"Conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"Conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"Conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the `microorganisms.codes` data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> `mo_uncertainties()` to review these uncertainties, or use #> `add_custom_microorganisms()` to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella #> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), #> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii #> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> Only the first 10 other matches of each record are shown. Run #> `print(mo_uncertainties(), n = ...)` to view more entries, or save #> `mo_uncertainties()` to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"Conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"Conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 730 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for `col_mo`. #> ℹ Using column 'date' as input for `col_date`. #> ℹ Using column 'patient_id' as input for `col_patient_id`. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,730 'phenotype-based' first isolates (91.0% of total where a #> microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,730 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,720 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"Conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2730 Length:2730 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-06 #> Mode :character Mode :character Median :2015-06-04 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-14 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :40.1% (n=1071) %S :51.1% (n=1354) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :17.0% (n=453) %I :12.7% (n=335) #> #2 :B_STPHY_AURS %R :42.9% (n=1147) %R :36.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.2% (n=1426) %S :60.7% (n=1656) TRUE:2730 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=178) %I : 3.0% (n=83) #> %R :41.2% (n=1126) %R :36.3% (n=991) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,730 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1854 4 4 4 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"Conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1326 #> 2 Staphylococcus aureus 684 #> 3 Streptococcus pneumoniae 401 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"Conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) #> # A tibble: 2,730 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,720 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,730 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,720 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,730 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,720 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) #> # A tibble: 991 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 981 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 461 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 451 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 461 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 451 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"Conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"Conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"Conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"Conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"Conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 example_isolates %>% top_n_microorganisms(n = 10) %>% group_by(age_group = age_groups(age, c(25, 50, 75)), gender) %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"Conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"Conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4294272 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.341 #> 2 B 0.586 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"Conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"Conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25) ggplot(my_data, aes(x = group, y = MIC, colour = SIR)) + geom_jitter(width = 0.2, size = 2) + geom_boxplot(fill = NA, colour = \"grey40\") + scale_y_mic() + scale_colour_sir() + labs(title = \"MIC Distribution and SIR Interpretation\", x = \"Sample Groups\", y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP
recipes
step_mic_log2()
<mic>
step_sir_numeric()
<sir>
ab_group()
all_groups
antibiogram()
TAN
FTA
antimicrobials
as.sir()
S
i
R
In case of set_ab_names() and data is a data.frame: columns to select (supports tidy selection such as column1:column4), otherwise other arguments passed on to as.ab().
set_ab_names()
data
column1:column4
as.ab()
A logical to indicate whether all antimicrobial groups must be return as a vector for each input value. For example, an antibiotic in the "aminopenicillins" group, is also in the "penicillins" and "beta-lactams" groups. Setting all_groups = TRUE would return all three for such an antibiotic, while all_groups = FALSE (default) only returns the most distinctive group name.
all_groups = TRUE
all_groups = FALSE
A logical to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group).