-eucast_rules() has become a wrapper around that function.
+eucast_rules() has become a wrapper around that function
+
Two new NA objects, NA_ab_ and NA_mo_, analogous to base R’s NA_character_ and NA_integer_, for use in pipelines that require typed missing values
-
Fixes
+
Fixes
Fixed a bug in as.ab() where certain AB codes containing “PH” or “TH” (such as ETH, MTH, PHE, PHN, STH, THA, THI1) would incorrectly return NA when combined in a vector with any untranslatable value (#245)
Fixed a bug in antibiogram() for when no antimicrobials are set
Fixed a bug in as.sir() where for numeric input the arguments S, I, and R would not be considered (#244)
@@ -94,7 +94,7 @@
Fixed Italian translation of CoNS to Stafilococco coagulasi-negativo and CoPS to Stafilococco coagulasi-positivo (#256)
-
Updates
+
Updates
susceptibility() and resistance() gained the argument guideline, which defaults to EUCAST, for interpreting the ‘I’ category correctly.
@@ -103,7 +103,7 @@
antimicrobials$group is now a list instead of a character, to contain any group the drug is in (#246)
ab_group() gained an argument all_groups to return all groups the antimicrobial drug is in (#246)
-
Added taniborbactam (TAN) and cefepime/taniborbactam (FTA) to the antimicrobials data set
+
Added to the antimicrobials data set: cefepime/taniborbactam (FTA), ceftibuten/avibactam (CTA), kasugamycin (KAS), ostreogrycin (OST), taniborbactam (TAN), thiostrepton (THS), xeruborbactam (XER), and zorbamycin (ZOR)
Added explaining message to as.sir() when interpreting numeric values (e.g., 1 for S, 2 for I, 3 for R) (#244)
Updated handling of capped MIC values (<, <=, >, >=) in as.sir() in the argument capped_mic_handling: (#243)
Introduced four clearly defined options: "none", "conservative" (default), "standard", and "lenient"
diff --git a/news/index.md b/news/index.md
index f9764b8d0..7aa46f76a 100644
--- a/news/index.md
+++ b/news/index.md
@@ -1,6 +1,6 @@
# Changelog
-## AMR 3.0.1.9023
+## AMR 3.0.1.9026
#### New
@@ -23,9 +23,6 @@
[`phosphonics()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
and
[`spiropyrimidinetriones()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
-- Antimicrobials in the `antimicrobials` data set: ceftibuten/avibactam
- (`CTA`), kasugamycin (`KAS`), ostreogrycin (`OST`), thiostrepton
- (`THS`), xeruborbactam (`XER`), zorbamycin (`ZOR`)
- Support for Wildtype (WT) / Non-wildtype (NWT) in
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md), all plotting
functions, and all susceptibility/resistance functions.
@@ -41,7 +38,10 @@
which allows future implementation of CLSI interpretive rules
([\#235](https://github.com/msberends/AMR/issues/235))
- [`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
- has become a wrapper around that function.
+ has become a wrapper around that function
+- Two new `NA` objects, `NA_ab_` and `NA_mo_`, analogous to base R’s
+ `NA_character_` and `NA_integer_`, for use in pipelines that require
+ typed missing values
#### Fixes
@@ -82,8 +82,10 @@
- [`ab_group()`](https://amr-for-r.org/reference/ab_property.md) gained
an argument `all_groups` to return all groups the antimicrobial drug
is in ([\#246](https://github.com/msberends/AMR/issues/246))
-- Added taniborbactam (`TAN`) and cefepime/taniborbactam (`FTA`) to the
- `antimicrobials` data set
+- Added to the `antimicrobials` data set: cefepime/taniborbactam
+ (`FTA`), ceftibuten/avibactam (`CTA`), kasugamycin (`KAS`),
+ ostreogrycin (`OST`), taniborbactam (`TAN`), thiostrepton (`THS`),
+ xeruborbactam (`XER`), and zorbamycin (`ZOR`)
- Added explaining message to
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) when
interpreting numeric values (e.g., 1 for S, 2 for I, 3 for R)
diff --git a/pkgdown.yml b/pkgdown.yml
index 52282ba06..a67250689 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
PCA: PCA.html
WHONET: WHONET.html
WISCA: WISCA.html
-last_built: 2026-03-04T08:06Z
+last_built: 2026-03-06T11:51Z
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 d39775da3..7fa7a3146 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 144ec05f7..1832a4c7c 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -9,7 +9,7 @@ options(AMR_guideline = "CLSI")'>AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/AMR.html b/reference/AMR.html
index bb8a5f376..e0da547fe 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/NA_ab_.html b/reference/NA_ab_.html
new file mode 100644
index 000000000..cef95015e
--- /dev/null
+++ b/reference/NA_ab_.html
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
diff --git a/reference/NA_mo_.html b/reference/NA_mo_.html
new file mode 100644
index 000000000..626779946
--- /dev/null
+++ b/reference/NA_mo_.html
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index 318d1cd02..5b7ab225b 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/WHONET.html b/reference/WHONET.html
index b4d38c9f9..c43c89a64 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 2defa0902..8ce1e6666 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 5e62071de..cdf26a9f8 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 6af469fb7..a134ac5f0 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index 268d17ef5..83657a8fa 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/age.html b/reference/age.html
index 85feaf7e9..e2a132711 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
@@ -112,16 +112,16 @@
df#> birth_date age age_exact age_at_y2k
-#> 1 1980-02-27 46 46.01370 19
-#> 2 1953-07-26 72 72.60548 46
-#> 3 1949-09-02 76 76.50137 50
-#> 4 1986-08-03 39 39.58356 13
-#> 5 1932-11-19 93 93.28767 67
-#> 6 1949-03-30 76 76.92877 50
-#> 7 1996-06-23 29 29.69589 3
-#> 8 1963-09-16 62 62.46301 36
-#> 9 1952-05-16 73 73.80000 47
-#> 10 1952-11-14 73 73.30137 47
+#> 1 1980-02-27 46 46.01918 19
+#> 2 1953-07-26 72 72.61096 46
+#> 3 1949-09-02 76 76.50685 50
+#> 4 1986-08-03 39 39.58904 13
+#> 5 1932-11-19 93 93.29315 67
+#> 6 1949-03-30 76 76.93425 50
+#> 7 1996-06-23 29 29.70137 3
+#> 8 1963-09-16 62 62.46849 36
+#> 9 1952-05-16 73 73.80548 47
+#> 10 1952-11-14 73 73.30685 47
+ab_reset_session()
+
+NA_ab_
@@ -101,6 +103,7 @@
Use the ab_* functions to get properties based on the returned antibiotic ID, see Examples.
Note: the as.ab() and ab_* functions may use very long regular expression to match brand names of antimicrobial drugs. This may fail on some systems.
NA_ab_ is a missing value of the new ab class, analogous to e.g. base R's NA_character_.
Source
diff --git a/reference/as.ab.md b/reference/as.ab.md
index 62d47fedf..c37f882d7 100644
--- a/reference/as.ab.md
+++ b/reference/as.ab.md
@@ -15,6 +15,8 @@ as.ab(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
is.ab(x)
ab_reset_session()
+
+NA_ab_
```
## Arguments
@@ -91,6 +93,9 @@ You can add your own manual codes to be considered by `as.ab()` and all
[`ab_*`](https://amr-for-r.org/reference/ab_property.md) functions, see
[`add_custom_antimicrobials()`](https://amr-for-r.org/reference/add_custom_antimicrobials.md).
+`NA_ab_` is a missing value of the new `ab` class, analogous to e.g.
+base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).
+
## Source
World Health Organization (WHO) Collaborating Centre for Drug Statistics
diff --git a/reference/as.av.html b/reference/as.av.html
index 124653877..175748825 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 6547b3c35..afeaecfd7 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 822ba75e3..91163f4e8 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 8233ccafd..af1326610 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
@@ -74,7 +74,9 @@
mo_reset_session()
-mo_cleaning_regex()
+mo_cleaning_regex()
+
+NA_mo_
@@ -201,6 +203,7 @@
This is based on:
Lancefield RC (1933). A serological differentiation of human and other groups of hemolytic streptococci.J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571
+
NA_mo_ is a missing value of the new mo class, analogous to e.g. base R's NA_character_.
Source
diff --git a/reference/as.mo.md b/reference/as.mo.md
index 7e40f8acd..db9ec847c 100644
--- a/reference/as.mo.md
+++ b/reference/as.mo.md
@@ -32,6 +32,8 @@ mo_failures()
mo_reset_session()
mo_cleaning_regex()
+
+NA_mo_
```
## Arguments
@@ -338,6 +340,9 @@ This is based on:
other groups of hemolytic streptococci.** *J Exp Med.* 57(4): 571-95;
[doi:10.1084/jem.57.4.571](https://doi.org/10.1084/jem.57.4.571)
+`NA_mo_` is a missing value of the new `mo` class, analogous to e.g.
+base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).
+
## Source
- Berends MS *et al.* (2022). **AMR: An R Package for Working with
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 719f5ccb2..cd39c4250 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)
- 3.0.1.9023
+ 3.0.1.9026
@@ -424,10 +424,10 @@ 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 2026-03-04 08:07:22 1 MIC amoxicillin Escherich… human 8
-#>2 2026-03-04 08:07:22 1 MIC cipro Escherich… human 0.256
-#>3 2026-03-04 08:07:23 1 DISK tobra Escherich… human 16
-#>4 2026-03-04 08:07:23 1 DISK genta Escherich… human 18
+#>1 2026-03-06 11:51:54 1 MIC amoxicillin Escherich… human 8
+#>2 2026-03-06 11:51:54 1 MIC cipro Escherich… human 0.256
+#>3 2026-03-06 11:51:55 1 DISK tobra Escherich… human 16
+#>4 2026-03-06 11:51:55 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 f79c718b3..fc979a259 100644
--- a/reference/as.sir.md
+++ b/reference/as.sir.md
@@ -660,10 +660,10 @@ sir_interpretation_history()
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#>
-#> 1 2026-03-04 08:07:22 1 MIC amoxicillin Escherich… human 8
-#> 2 2026-03-04 08:07:22 1 MIC cipro Escherich… human 0.256
-#> 3 2026-03-04 08:07:23 1 DISK tobra Escherich… human 16
-#> 4 2026-03-04 08:07:23 1 DISK genta Escherich… human 18
+#> 1 2026-03-06 11:51:54 1 MIC amoxicillin Escherich… human 8
+#> 2 2026-03-06 11:51:54 1 MIC cipro Escherich… human 0.256
+#> 3 2026-03-06 11:51:55 1 DISK tobra Escherich… human 16
+#> 4 2026-03-06 11:51:55 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 67b126f1b..bad896e70 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 06d886aa0..591d91ea5 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/av_property.html b/reference/av_property.html
index ef13477bb..69724301c 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/availability.html b/reference/availability.html
index d0d7465eb..ee05c4667 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index b50fe273a..9c47bc9f7 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 28d15a71c..a45826c17 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/count.html b/reference/count.html
index cbe2cf8a6..93c01c45f 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 80b3da691..535cb9ca8 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html
index 5582a28b6..b49a7815d 100644
--- a/reference/custom_mdro_guideline.html
+++ b/reference/custom_mdro_guideline.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/dosage.html b/reference/dosage.html
index c8b2d4fc7..389e26066 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html
index 1a90c1de1..95b72da71 100644
--- a/reference/esbl_isolates.html
+++ b/reference/esbl_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 224259b7d..0fb28038d 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 5332829f6..0fd135030 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index 1480d1794..8616e0bf9 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 83958b0c5..6fa28b2b8 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/g.test.html b/reference/g.test.html
index 0296c7f60..9de8a42ca 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/get_episode.html b/reference/get_episode.html
index a19effb7e..13eb1089a 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 648a99c21..6777c05ae 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index ccf614f05..db76f17fe 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index 102fa6edc..5d9ba30a5 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/index.html b/reference/index.html
index faa2bcf30..2958e26f1 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
@@ -75,7 +75,7 @@
diff --git a/reference/index.md b/reference/index.md
index db83d36ac..634b780ce 100644
--- a/reference/index.md
+++ b/reference/index.md
@@ -29,8 +29,9 @@ to add your own custom microorganisms to this package.
[`mo_renamed()`](https://amr-for-r.org/reference/as.mo.md)
[`mo_failures()`](https://amr-for-r.org/reference/as.mo.md)
[`mo_reset_session()`](https://amr-for-r.org/reference/as.mo.md)
- [`mo_cleaning_regex()`](https://amr-for-r.org/reference/as.mo.md) :
- Transform Arbitrary Input to Valid Microbial Taxonomy
+ [`mo_cleaning_regex()`](https://amr-for-r.org/reference/as.mo.md)
+ [`NA_mo_`](https://amr-for-r.org/reference/as.mo.md) : Transform
+ Arbitrary Input to Valid Microbial Taxonomy
- [`mo_name()`](https://amr-for-r.org/reference/mo_property.md)
[`mo_fullname()`](https://amr-for-r.org/reference/mo_property.md)
[`mo_shortname()`](https://amr-for-r.org/reference/mo_property.md)
@@ -85,8 +86,9 @@ from clinical text records, using
- [`as.ab()`](https://amr-for-r.org/reference/as.ab.md)
[`is.ab()`](https://amr-for-r.org/reference/as.ab.md)
- [`ab_reset_session()`](https://amr-for-r.org/reference/as.ab.md) :
- Transform Input to an Antibiotic ID
+ [`ab_reset_session()`](https://amr-for-r.org/reference/as.ab.md)
+ [`NA_ab_`](https://amr-for-r.org/reference/as.ab.md) : Transform Input
+ to an Antibiotic ID
- [`ab_name()`](https://amr-for-r.org/reference/ab_property.md)
[`ab_cid()`](https://amr-for-r.org/reference/ab_property.md)
[`ab_synonyms()`](https://amr-for-r.org/reference/ab_property.md)
diff --git a/reference/interpretive_rules.html b/reference/interpretive_rules.html
index 553c3a0e7..d1cc93fee 100644
--- a/reference/interpretive_rules.html
+++ b/reference/interpretive_rules.html
@@ -13,7 +13,7 @@ To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 98a80e310..8ee0b9252 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 342a98cfe..2334f3dea 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/join.html b/reference/join.html
index a3ad5bdd0..fe6cc150c 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 88d027f97..b261fcccf 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index d45da1445..a56cc2d7a 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/like.html b/reference/like.html
index 9d3761459..96cec54ef 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/mdro.html b/reference/mdro.html
index 325949270..2ae03c1e2 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 29653a709..94f3df54b 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index c6af187b5..127c70584 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 6ed89e2c2..216b00740 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 6f0d57752..d0a35c3a0 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index af3b4fa4b..9db6a63f1 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 6ddfd9f5c..962e80111 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/mo_source.html b/reference/mo_source.html
index ce6934baa..d99f91355 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/pca.html b/reference/pca.html
index c36b33f22..b44c8cd25 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/plot.html b/reference/plot.html
index 71e358d75..825aa4411 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/proportion.html b/reference/proportion.html
index 568f333ed..7e60072b7 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/random.html b/reference/random.html
index 6327ca6e2..7bfe3916e 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 06f5e1461..a7624d180 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/skewness.html b/reference/skewness.html
index 98760bc81..c38847355 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)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index 479ffb433..fe93385cf 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/reference/translate.html b/reference/translate.html
index 89a13a210..b896df24a 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9023
+ 3.0.1.9026
diff --git a/search.json b/search.json
index daed509f8..77a181bd1 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/CLAUDE.html","id":null,"dir":"","previous_headings":"","what":"CLAUDE.md — AMR R Package","title":"CLAUDE.md — AMR R Package","text":"file provides context Claude Code working repository.","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"project-overview","dir":"","previous_headings":"","what":"Project Overview","title":"CLAUDE.md — AMR R Package","text":"AMR zero-dependency R package antimicrobial resistance (AMR) data analysis using One Health approach. peer-reviewed, used 175+ countries, supports 28 languages. Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) disk diffusion handling - Multilingual output (28 languages)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"common-commands","dir":"","previous_headings":"","what":"Common Commands","title":"CLAUDE.md — AMR R Package","text":"commands run inside R session: shell:","code":"# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE) devtools::document() # Run all tests devtools::test() # Full package check (CRAN-level: docs + tests + checks) devtools::check() # Build pkgdown website locally pkgdown::build_site() # Code coverage report covr::package_coverage() # CRAN check from parent directory R CMD check AMR"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"repository-structure","dir":"","previous_headings":"","what":"Repository Structure","title":"CLAUDE.md — AMR R Package","text":"","code":"R/ # All R source files (62 files, ~28,000 lines) man/ # Auto-generated .Rd documentation (do not edit manually) tests/testthat/ # testthat test files (test-*.R) and helper-functions.R data/ # Pre-compiled .rda datasets data-raw/ # Scripts used to generate data/ files vignettes/ # Rmd vignette articles inst/ # Installed files (translations, etc.) _pkgdown.yml # pkgdown website configuration"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"r-source-file-conventions","dir":"","previous_headings":"","what":"R Source File Conventions","title":"CLAUDE.md — AMR R Package","text":"Naming conventions R/: Key source files: aa_helper_functions.R / aa_helper_pm_functions.R — internal utility functions (large; ~63 KB ~37 KB) aa_globals.R — global constants breakpoint lookup structures aa_options.R — amr_options() / get_AMR_option() system mo.R / mo_property.R — microorganism lookup properties ab.R / ab_property.R — antimicrobial drug functions av.R / av_property.R — antiviral drug functions sir.R / sir_calc.R / sir_df.R — SIR classification engine mic.R / disk.R — MIC disk diffusion classes antibiogram.R — antibiogram generation (traditional, combined, syndromic, WISCA) first_isolate.R — first-isolate identification algorithms mdro.R — MDRO classification (EUCAST, CLSI, CDC, custom guidelines) amr_selectors.R — tidyselect helpers selecting AMR columns interpretive_rules.R / custom_eucast_rules.R — clinical interpretation rules translate.R — 28-language translation system ggplot_sir.R / ggplot_pca.R / plotting.R — visualisation functions","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"custom-s3-classes","dir":"","previous_headings":"","what":"Custom S3 Classes","title":"CLAUDE.md — AMR R Package","text":"package defines five S3 classes full print/format/plot/vctrs support:","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"data-files","dir":"","previous_headings":"","what":"Data Files","title":"CLAUDE.md — AMR R Package","text":"Pre-compiled data/ (edit directly; regenerate via data-raw/ scripts):","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"zero-dependency-design","dir":"","previous_headings":"","what":"Zero-Dependency Design","title":"CLAUDE.md — AMR R Package","text":"package Imports DESCRIPTION. optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) listed Suggests guarded : Never add packages Imports. new functionality requires external package, add Suggests guard usage appropriately.","code":"if (requireNamespace(\"pkg\", quietly = TRUE)) { ... }"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"testing","dir":"","previous_headings":"","what":"Testing","title":"CLAUDE.md — AMR R Package","text":"Framework: testthat (R ≥ 3.1); legacy tinytest used R 3.0–3.6 CI Test files: tests/testthat/test-*.R Helpers: tests/testthat/helper-functions.R CI matrix: GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 oldrel-4 Coverage: covr (files excluded: atc_online.R, mo_source.R, translate.R, resistance_predict.R, zz_deprecated.R, helper files, zzz.R)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"CLAUDE.md — AMR R Package","text":"exported functions use roxygen2 blocks (RoxygenNote: 7.3.3, markdown enabled) Run devtools::document() change roxygen comments Never edit files man/ directly — auto-generated Vignettes live vignettes/ .Rmd files pkgdown website configured _pkgdown.yml","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"versioning","dir":"","previous_headings":"","what":"Versioning","title":"CLAUDE.md — AMR R Package","text":"Version format: major.minor.patch.dev (e.g., 3.0.1.9021) Development versions use .9xxx suffix Stable CRAN releases drop dev suffix (e.g., 3.0.1) NEWS.md uses sections New, Fixes, Updates GitHub issue references (#NNN)","code":""},{"path":"https://amr-for-r.org/CLAUDE.html","id":"version-and-date-bump-required-for-every-pr","dir":"","previous_headings":"Versioning","what":"Version and date bump required for every PR","title":"CLAUDE.md — AMR R Package","text":"opening pull request, always increment four-digit dev counter 1 files: DESCRIPTION — Version: field: NEWS.md — top-level heading: Read current version DESCRIPTION, add 1 last numeric component, write new version files commit rest PR changes. Also bump date current date DESCRIPTION, ’s Date: field ISO format:","code":"Version: 3.0.1.9021 → Version: 3.0.1.9022 # AMR 3.0.1.9021 → # AMR 3.0.1.9022 Date: 2025-12-31"},{"path":"https://amr-for-r.org/CLAUDE.html","id":"internal-state","dir":"","previous_headings":"","what":"Internal State","title":"CLAUDE.md — AMR R Package","text":"package uses private AMR_env environment (created aa_globals.R) caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). avoids re-computation within session.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial drugs, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"Conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"Conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"Conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 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 Column 'first' is SIR eligible (despite only having empty values), since #> it seems to be cefozopran (ZOP) #> ℹ Using column 'date' as input for `col_date`. #> ℹ Using column 'patient_id' as input for `col_patient_id`. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,730 '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) #> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I' #> category susceptible. Set the `guideline` argument or the `AMR_guideline` #> option to either \"CLSI\" or \"EUCAST\", see `?AMR-options`. #> ℹ This message will be shown once per session. #> [1] 0.4294272 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 , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modelling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22 e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC