Download data sets for download / own use
-03 July 2026
+09 July 2026
Source:vignettes/datasets.Rmd
datasets.Rmdab, cid, name, group, a iv_ddd, iv_units, and loinc.
This data set is in R available as antimicrobials, after
you load the AMR package.
It was last updated on 27 June 2026 12:31:58 UTC. Find more info
-about the contents, (scientific) source, and structure of this data set
+ It was last updated on 9 July 2026 15:14:59 UTC. Find more info about
+the contents, (scientific) source, and structure of this data set
here. Direct download links: A data set with 45 555 rows and 14 columns, containing the following
+ A data set with 45 735 rows and 14 columns, containing the following
column names: This data set is in R available as It was last updated on 22 June 2026 23:38:13 UTC. Find more info
-about the contents, (scientific) source, and structure of this data
+ It was last updated on 9 July 2026 15:14:59 UTC. Find more info about
+the contents, (scientific) source, and structure of this data
set here. Direct download links:
@@ -612,20 +612,20 @@ inhibitors
(2.7 MB)clinical_breakpoints: Interpretation from MIC values
& disk diameters to SIR
-
guideline, type, host, method,
site, mo, rank_index, ab,
ref_tbl, disk_dose, breakpoint_S,
breakpoint_R, uti, and is_SDD.clinical_breakpoints,
after you load the AMR package.
(93 kB)
+R Data Structure (RDS) file
@@ -637,7 +637,7 @@ Excel workbook
Feather file (2 MB)
+Parquet file (0.2 MB)
diff --git a/articles/datasets.md b/articles/datasets.md index a9711cb80..3d58bde37 100644 --- a/articles/datasets.md +++ b/articles/datasets.md @@ -100,7 +100,7 @@ names: This data set is in R available as `antimicrobials`, after you load the `AMR` package. -It was last updated on 27 June 2026 12:31:58 UTC. Find more info about +It was last updated on 9 July 2026 15:14:59 UTC. Find more info about the contents, (scientific) source, and structure of this [data set here](https://amr-for-r.org/reference/antimicrobials.html). @@ -147,7 +147,7 @@ as comma separated values. ## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR -A data set with 45 555 rows and 14 columns, containing the following +A data set with 45 735 rows and 14 columns, containing the following column names: *guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*, *ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and @@ -156,7 +156,7 @@ column names: This data set is in R available as `clinical_breakpoints`, after you load the `AMR` package. -It was last updated on 22 June 2026 23:38:13 UTC. Find more info about +It was last updated on 9 July 2026 15:14:59 UTC. Find more info about the contents, (scientific) source, and structure of this [data set here](https://amr-for-r.org/reference/clinical_breakpoints.html). @@ -164,7 +164,7 @@ here](https://amr-for-r.org/reference/clinical_breakpoints.html). - Download as [original R Data Structure (RDS) file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds) - (92 kB) + (93 kB) - Download as [tab-separated text file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt) (4.2 MB) @@ -176,7 +176,7 @@ here](https://amr-for-r.org/reference/clinical_breakpoints.html). (2 MB) - Download as [Apache Parquet file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet) - (0.1 MB) + (0.2 MB) - Download as [IBM SPSS Statistics data file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav) (7.5 MB) diff --git a/articles/index.html b/articles/index.html index 22dcfaffe..0f5f3f2ed 100644 --- a/articles/index.html +++ b/articles/index.html @@ -7,7 +7,7 @@ AMR (for R) - 3.0.1.9083 + 3.0.1.9084
Introduction
The AMR package is a peer-reviewed, free and open-source R package with zero dependencies to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of many different researchers from around the globe to make this a successful and durable project! The AMR package was already cited over 100 times in scientific research.
After installing this package, R knows ~97 000 distinct microbial species (updated mei 2026) and all ~620 antimicrobial and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen and the University Medical Center Groningen.
+After installing this package, R knows ~97 000 distinct microbial species (updated May 2026) and all ~620 antimicrobial and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen and the University Medical Center Groningen.
Used in over 175 countries, available in 28 languages
@@ -145,13 +145,11 @@ #> ℹ Using column mo as input for `mo_fullname()` #> ℹ Using column mo as input for `mo_is_gram_negative()` #> ℹ Using column mo as input for `mo_is_intrinsic_resistant()` -#> ℹ Determining intrinsic resistance based on 'EUCAST Expected -#> Resistant Phenotypes' v1.2 (2023). This note will be shown -#> once per session. -#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB -#> (tobramycin), AMK (amikacin), and KAN (kanamycin) -#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM -#> (meropenem) +#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). +#> This note will be shown once per session. +#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN +#> (kanamycin) +#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem) #> # A tibble: 35 × 7 #> bacteria GEN TOB AMK KAN IPM MEM #> <chr> <sir> <sir> <sir> <sir> <sir> <sir> @@ -180,9 +178,9 @@ #> Warning: invalid microorganism code, NA generated| Piperacillin/tazobactam | @@ -190,9 +188,9 @@Piperacillin/tazobactam + Tobramycin | ||||
|---|---|---|---|---|---|
| 70.1% (65.1-75.4%) | -93.6% (92.1-95%) | -89.8% (87.3-92.4%) | +70% (64.8-75.2%) | +93.6% (92-95.1%) | +89.9% (87.1-92.5%) |
WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:
@@ -204,10 +202,10 @@ #> Warning: invalid microorganism code, NA generated| Syndromic Group | @@ -218,21 +216,21 @@||||||
|---|---|---|---|---|---|---|
| Clinical | -74.4% (68.2-79.9%) | -93.6% (91.9-95.1%) | -90.4% (86.9-93.3%) | +74.6% (69.3-80.3%) | +93.6% (92.1-95%) | +90.4% (87-93.2%) |
| ICU | -57% (48.6-65.9%) | -86.8% (83.4-89.8%) | -82.9% (77.5-87.1%) | +56.9% (48.2-66.3%) | +86.7% (83.4-89.7%) | +82.9% (78.1-87.3%) |
| Outpatient | -57.5% (45.9-69.3%) | -76.6% (70.6-82.3%) | -67.9% (57.6-77.2%) | +57.3% (45.8-69.1%) | +76.6% (70.6-81.9%) | +67.9% (58-76.9%) |
antibiogram(example_isolates,
mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP"))
-#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
-#> (meropenem)